Messages

1

Event Management. / AI and Agentic AI in Event Management: A Bangladesh Perspective

« on: April 15, 2026, 03:30:09 AM »

Artificial Intelligence and the emerging concept of Agentic AI are transforming event management globally, and Bangladesh is in a strong position to benefit from these technologies. Event management involves planning, coordination, marketing, logistics, and real time decision making, all of which can be significantly improved through intelligent systems. In Bangladesh, where the event industry is growing rapidly with corporate events, weddings, exhibitions, and cultural programs, AI can bring efficiency, scalability, and professionalism.

AI can first assist in event planning and organization by analyzing past data and suggesting optimal venues, budgets, schedules, and vendor selections. For example, event planners in Dhaka can use AI tools to predict guest attendance, recommend suitable locations based on capacity and cost, and optimize timelines. This reduces manual effort and minimizes planning errors.

Another important area is marketing and audience targeting. AI can analyze social media behavior, preferences, and engagement patterns to create targeted promotional campaigns. In Bangladesh, where platforms like Facebook and YouTube dominate digital communication, AI driven marketing can help event organizers reach the right audience more effectively and increase participation.

AI also improves guest experience and communication. Chatbots can handle inquiries, send invitations, provide real time updates, and assist guests with directions or schedules. For large events such as trade fairs or university programs, this reduces the need for extensive human support and ensures smooth communication.

The concept of Agentic AI takes this a step further. Unlike traditional AI, Agentic AI systems can act autonomously, make decisions, and coordinate multiple tasks. In event management, an agentic system could automatically book vendors, adjust schedules based on delays, manage ticketing systems, and even respond to unexpected issues such as weather disruptions. For example, if traffic congestion is detected in Dhaka, an agentic system could notify attendees, adjust event timing, and coordinate with transport services without human intervention.

In logistics and operations, AI can optimize resource management such as seating arrangements, food distribution, and crowd control. During large scale events like trade expos or concerts, AI powered systems can monitor crowd density and improve safety by predicting overcrowding risks.

However, in the context of Bangladesh, there are several challenges. One major issue is the lack of technical infrastructure and awareness. Many event management companies still rely on manual processes and have limited exposure to AI tools. Another challenge is data availability, as AI systems require quality data to function effectively. Additionally, there is a shortage of skilled professionals who can implement and manage AI based systems.

To overcome these challenges, Bangladesh should focus on several key steps. First, event management companies should gradually adopt digital tools and AI platforms for planning and communication. Second, training programs and workshops should be introduced to develop skills in AI and event technology. Third, collaboration between tech startups and event organizers can help create locally relevant AI solutions. Finally, government and private sector support can encourage innovation through funding and digital infrastructure development.

In conclusion, AI and Agentic AI have the potential to significantly improve event management in Bangladesh by making processes smarter, faster, and more efficient. While challenges exist, proper investment in technology, skills, and awareness can help the country leverage these advancements and modernize its event industry.


S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/

2

Logo Design. / Building Identity Through Design: The Role of Logos in Branding in Bangladesh

« on: April 15, 2026, 03:28:18 AM »

A logo is not just a visual symbol; it is the face of a brand and a powerful tool for communication. Branding, on the other hand, is the overall perception and emotional connection that people develop with a company or product. The connection between logo and brand is therefore deeply interdependent. A well designed logo helps establish brand identity, while consistent branding gives meaning and value to the logo. In the context of Bangladesh, this relationship is becoming increasingly important as local businesses expand into competitive and global markets.
In Bangladesh, many small and medium enterprises still treat logos as decorative elements rather than strategic assets. However, global branding practices show that a logo should reflect the core values, mission, and positioning of a brand. For example, companies like Grameenphone and bKash have successfully used simple yet meaningful logos to build strong brand recognition. Their logos are not complex, but they are consistent, memorable, and aligned with their brand identity, which helps customers easily associate the visual mark with trust and service quality.
The logic behind the connection between logo and brand lies in human psychology and communication. A logo acts as a visual shortcut that allows consumers to quickly identify and recall a brand. According to branding theory, consistent visual identity increases brand recognition and trust over time. In Bangladesh, where markets are becoming more saturated, this recognition is crucial for standing out. A strong logo creates first impressions, while consistent branding across packaging, advertising, and digital platforms reinforces that impression.
However, there are challenges in the Bangladeshi context. Many businesses lack awareness about professional branding and often rely on low cost or generic logo designs. This leads to poor differentiation and weak brand identity. Additionally, there is limited collaboration between designers and business strategists, which results in logos that look visually appealing but fail to communicate the brand’s purpose. Research in brand identity design suggests that effective logos must be simple, relevant, and adaptable across different media, yet many local brands do not follow these principles.
To improve this situation, several steps should be taken. First, businesses in Bangladesh need to understand that investing in branding is not a luxury but a necessity for long term growth. A logo should be developed as part of a broader branding strategy, not as an isolated design. Second, designers should focus on research driven design processes, where they analyze the target audience, cultural context, and market positioning before creating a logo. Third, educational institutions and training programs should emphasize branding theory along with design skills, so that future professionals can bridge the gap between creativity and strategy.
Another important step is to embrace digital readiness. Since most brand interactions now occur online, logos must be scalable, simple, and effective across digital platforms such as mobile apps and social media. Bangladeshi brands should also maintain consistency in color, typography, and messaging to strengthen their identity. Over time, this consistency builds trust and loyalty among consumers.
In conclusion, the connection between logo and brand is critical for business success, especially in a growing market like Bangladesh. A logo is not just a design element but a strategic tool that represents the brand’s identity and values. By adopting a more thoughtful and research based approach to branding, Bangladeshi businesses can create stronger, more recognizable identities and compete effectively both locally and globally.

References
Wheeler, A. (2017). Designing Brand Identity (5th ed.). Wiley.
Keller, K. L. (2013). Strategic Brand Management. Pearson.
Henderson, P. W., & Cote, J. A. (1998). “Guidelines for selecting or modifying logos.” Journal of Marketing.
Bangladesh branding examples such as Grameenphone and bKash corporate identity reports
Nielsen Norman Group. “Branding and User Experience”




S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/

3

Logo Design. / The Rise of Minimal and Solid Colored Logos: Logic, Context, and Industry Trends

« on: April 15, 2026, 03:26:25 AM »

In recent years, simple, solid colored, and minimal logos have become increasingly popular across industries. This shift is not merely a design trend but a result of deeper technological, psychological, and branding considerations. The move toward minimalism reflects how brands adapt to digital environments, consumer behavior, and the need for strong visual identity in a crowded marketplace.
One of the primary reasons for the popularity of minimal logos is digital adaptability. Modern branding must function across multiple platforms, including mobile devices, social media, websites, and apps. Complex logos with gradients, textures, and fine details often lose clarity when scaled down. In contrast, simple and solid colored logos remain clear and recognizable even at very small sizes, such as app icons or profile images. According to design research, scalability and responsiveness have become essential requirements in logo design in the digital era.
Another important factor is cognitive psychology and visual perception. Human brains process simple shapes and colors more quickly than complex visuals. Minimal logos reduce cognitive load, making them easier to remember and recognize. Studies in visual cognition suggest that simplicity enhances memory retention and brand recall, which is crucial for marketing effectiveness. This is why companies like Apple, Nike, and Google have progressively simplified their logos over time.
The rise of brand clarity and universality also plays a significant role. In a globalized market, logos must communicate across different languages and cultures. Minimal designs rely on basic shapes and strong colors, which are more universally understood than intricate symbols or text heavy designs. This allows brands to maintain consistency across international markets without confusion.
From a practical standpoint, cost efficiency and versatility contribute to this trend. Simple logos are easier to reproduce across different mediums such as print, packaging, digital screens, and merchandise. They require fewer variations and maintain consistency in both color and form. Solid colors also perform better in monochrome or limited color printing, which reduces production costs.
Another key driver is the influence of modern design philosophy, particularly minimalism and flat design. These movements emphasize clarity, functionality, and the removal of unnecessary elements. With the decline of skeuomorphic design and the rise of flat and material design in user interfaces, brands have aligned their visual identities to match these aesthetics. This creates a cohesive experience between product design and branding.
There is also a strong connection with attention economy and branding strategy. In an age where users are exposed to massive amounts of visual content daily, brands have only a few seconds to capture attention. Minimal logos stand out because they are clean and instantly recognizable. Their simplicity allows them to be more flexible in animations, motion graphics, and dynamic branding systems used in modern media.
Despite these advantages, minimal logos are not without criticism. Some argue that excessive simplification can lead to loss of uniqueness, making brands look similar. However, successful minimal logos balance simplicity with distinctiveness through careful use of proportion, color, and typography.
In conclusion, the growing popularity of simple, solid colored, and minimal logos is driven by logical factors including digital scalability, cognitive efficiency, global communication, cost effectiveness, and alignment with modern design trends. Rather than being just a stylistic choice, minimalism in logo design reflects a strategic response to the evolving demands of technology and human perception.

References
Lidwell, W., Holden, K., & Butler, J. (2010). Universal Principles of Design. Rockport Publishers.
Norman, D. A. (2013). The Design of Everyday Things. Basic Books.
Wheeler, A. (2017). Designing Brand Identity (5th ed.). Wiley.
Henderson, P. W., & Cote, J. A. (1998). “Guidelines for selecting or modifying logos.” Journal of Marketing.
Google Material Design Guidelines (design.google)
Nielsen Norman Group. “Visual Design Basics for User Interfaces.”



S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/

4

Object dynamics. / Object Dynamics and Physics Based Animation: Interconnection and Significance

« on: April 15, 2026, 03:24:00 AM »

Object dynamics and physics based animation are closely related concepts within computer graphics and simulation, both aiming to replicate realistic motion of objects by following the laws of physics. Object dynamics refers to the study of how objects move and interact under forces such as gravity, collision, friction, and external influences. Physics based animation, on the other hand, is the practical application of these physical principles in computer graphics to create realistic motion in digital environments such as films, games, and simulations.
The interconnection between object dynamics and physics based animation lies in their shared foundation. Object dynamics provides the theoretical and mathematical framework, typically derived from Newtonian mechanics, while physics based animation uses this framework to generate motion automatically rather than relying on manual keyframing. In this sense, physics based animation can be viewed as an implementation of object dynamics within a computational environment. For example, when a ball falls, bounces, and rolls in an animation, its motion is governed by equations of object dynamics, including force, mass, acceleration, and collision response.
A key aspect of this relationship is the use of rigid body dynamics and deformable body dynamics. Rigid body dynamics deals with solid objects that do not deform, such as rocks or vehicles, while deformable dynamics handles flexible objects such as cloth or soft materials. Physics based animation systems incorporate these models to simulate realistic interactions between objects, including collisions, constraints, and energy transfer. This allows animators to produce complex scenes where objects behave naturally without explicitly animating every movement.
Another important connection is the use of numerical methods and simulation techniques. Since real world physics equations are often too complex to solve analytically in animation, computational methods such as time integration, collision detection, and constraint solvers are used. These methods translate the principles of object dynamics into algorithms that can run efficiently on computers. Modern animation software like Blender, Maya, and Houdini includes built in physics engines that automate these processes.
In recent developments, Artificial Intelligence is further strengthening this interconnection. AI techniques are being used to enhance physics based animation by learning motion patterns, predicting object behavior, and improving simulation efficiency. For instance, machine learning models can approximate dynamic systems or assist in controlling physically simulated characters, making animations both realistic and computationally efficient.
Despite their advantages, there are still challenges. Physics based animation can be computationally expensive, especially for complex systems with many interacting objects. Additionally, achieving artistic control while maintaining physical accuracy can be difficult, as strict adherence to physics may not always produce the desired visual effect. Therefore, many systems combine physics based methods with artistic adjustments.
In conclusion, object dynamics and physics based animation are fundamentally interconnected, with one providing the theoretical basis and the other serving as its practical implementation in computer graphics. Their integration enables the creation of realistic, efficient, and dynamic animations, and continues to evolve with advancements in simulation techniques and Artificial Intelligence.

References
Baraff, D., & Witkin, A. (1998). Large steps in cloth simulation. Proceedings of SIGGRAPH.
Bridson, R. (2015). Fluid Simulation for Computer Graphics. CRC Press.
Eberly, D. (2003). Game Physics. Morgan Kaufmann.
Millington, I. (2010). Game Physics Engine Development. CRC Press.
Müller, M., Heidelberger, B., Hennix, M., & Ratcliff, J. (2007). Position based dynamics. Journal of Visual Communication and Image Representation.
Witkin, A., & Kass, M. (1988). Spacetime constraints. Proceedings of SIGGRAPH.
Nealen, A., Müller, M., Keiser, R., Boxerman, E., & Carlson, M. (2006). Physically based deformable models in computer graphics. Computer Graphics Forum.





S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/

5

Simulation expert. / Simulation, Artificial Intelligence, and CGI: A Modern Integrated Perspective

« on: April 15, 2026, 03:21:55 AM »

Simulation, Artificial Intelligence, and Computer Generated Imagery are now deeply interconnected fields that together define the future of digital content creation. Traditionally, CGI relied on physics based simulation to recreate natural phenomena such as fluid motion, smoke, fire, and object dynamics. These simulations were driven by mathematical models such as partial differential equations, which required heavy computation and time consuming numerical methods. However, recent advancements show a clear shift toward integrating AI with simulation to make CGI faster, smarter, and more realistic.
At the core of CGI simulation lies the idea of physically based modeling, where real world behavior is replicated digitally. This includes simulating fluids, cloth, rigid bodies, and environmental effects. However, solving these systems using classical numerical approaches is computationally expensive and often limits real time performance. This is where Artificial Intelligence has started to play a transformative role. AI models, especially deep learning systems, are now being used to approximate solutions to complex equations, reducing the need for expensive computations while maintaining acceptable accuracy. Research shows that AI for solving physical equations such as fluid dynamics can significantly accelerate simulations by learning patterns directly from data instead of computing from scratch.
One of the most important recent developments is the emergence of AI driven simulation models. These models combine machine learning with traditional physics engines to create hybrid systems. Instead of replacing physics entirely, AI enhances simulation pipelines by predicting motion, filling in missing details, and improving stability. For example, neural network based fluid simulation models can achieve massive speed improvements compared to traditional methods while maintaining realistic motion behavior.
In the CGI industry, especially in film, gaming, and virtual reality, AI has also revolutionized rendering and animation. Generative AI techniques can now create textures, 3D objects, and even entire scenes from simple inputs. Recent developments presented in global conferences such as SIGGRAPH show that AI can generate highly realistic 3D environments and improve rendering efficiency, making real time simulation more achievable than ever before. Similarly, modern research pipelines integrate AI into animation, geometry processing, and physics simulation, demonstrating how learning based approaches are becoming central to computer graphics workflows.
Another breakthrough area is the concept of “world models” in AI, where systems can simulate entire environments in real time. Recent AI models are capable of generating interactive 3D worlds from text prompts and simulating physical interactions dynamically. These systems are already being used in areas like autonomous driving simulations and virtual environments, indicating a future where CGI and simulation are fully AI driven.
Despite these advancements, there are still important limitations. AI based simulations can sometimes produce visually convincing but physically incorrect results. Studies have shown that generative AI models may fail to accurately represent fluid motion or complex physical phenomena due to limitations in training data and understanding of physics. Moreover, many industries are still in the experimental stage of adopting AI, with most applications not yet fully scaled across production systems.
Logically, the relationship between simulation, AI, and CGI can be understood as a progression. First, traditional simulation provided the foundation for realism in graphics. Then, CGI enabled visualization and creative control over simulated phenomena. Now, AI acts as an accelerator and enhancer, improving both efficiency and realism while opening new possibilities such as real time interactive worlds and automated content creation.
In conclusion, the integration of simulation, Artificial Intelligence, and CGI represents a major paradigm shift in digital technology. AI is not replacing simulation but transforming how it is performed, making it faster, more scalable, and more accessible. The future of CGI lies in this hybrid approach, where physics based accuracy and data driven intelligence work together to create highly realistic and interactive digital experiences.

References
Review on AI for solving physical equations and simulation methods
Neural network based fluid simulation research
SIGGRAPH research on AI and CGI advancements
Stanford course on AI in computer graphics
AI world models and real time simulation
Limitations of generative AI in fluid simulation
Global AI adoption trends




S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/

6

Simulation expert. / Simulation in the CG Industry in Bangladesh: Context, Challenges, and beyond

« on: April 15, 2026, 03:20:28 AM »

Simulation in the computer graphics industry refers to the use of computational techniques to recreate real world phenomena such as fluid motion, smoke, fire, and environmental effects for animation, films, advertisements, and digital media. In Bangladesh, the CG industry is still developing but has shown promising growth in recent years. Studios like DFX Studio and Firedrum Studios are already working with advanced techniques such as fluid simulation and visual effects for commercials and cinematic productions. These studios demonstrate that Bangladesh has entered the global pipeline of CGI, VFX, and animation production, often contributing to outsourced international projects.
The broader animation and CG sector in Bangladesh is considered a growing but still emerging industry. It employs thousands of people and is increasingly connected to global markets through outsourcing and digital platforms. However, simulation technologies such as fluid dynamics, physics based animation, and real time rendering are not yet widely advanced or standardized across the industry. Most local studios focus on production and visual output rather than deep simulation research or high end physics based systems.
One of the major challenges in Bangladesh’s CG simulation sector is the lack of advanced technical infrastructure and research integration. While universities like the University of Dhaka and BUET conduct research in fluid flow modeling and simulation, this knowledge is not effectively transferred into the creative industry. Additionally, the overall computational simulation market in Bangladesh is still developing, indicating limited adoption of high performance computing tools and advanced simulation software.
Another significant limitation is the shortage of skilled professionals who specialize in both physics based simulation and computer graphics. Most artists are trained in design tools but lack deep knowledge of simulation physics, while engineers often do not work in creative industries. This gap creates a barrier in producing high quality, physically realistic simulations such as fluid effects seen in international films and games. Furthermore, many studios rely on pre built tools rather than developing custom simulation systems, which limits innovation.
There is also a financial and awareness barrier. Simulation tools and software such as Houdini or advanced rendering engines require strong hardware and investment, which many small and medium studios cannot afford. As a result, production pipelines often prioritize speed and cost over realism and research driven simulation.
To overcome these challenges, several steps should be taken by Bangladesh as a nation and by individuals working in the CG industry. First, there must be stronger collaboration between universities and industry. Research in fluid dynamics, simulation, and AI should be integrated into animation and VFX production pipelines. Joint projects, internships, and research based studios can help bridge the gap between theory and practice.
Second, investment in education and skill development is essential. Training programs should focus not only on software use but also on the underlying physics and mathematics of simulation. Learning tools like Houdini, Blender physics engines, and real time simulation frameworks will help artists compete globally. Inspiration can be drawn from successful Bangladeshi professionals such as Nafees Bin Zafar, who contributed to fluid simulation systems in Hollywood and received an Academy Award for his work.
Third, government and private sector support is needed to improve infrastructure. This includes funding for high performance computing, research labs, and startup studios focused on simulation and VFX technology. Policies that encourage digital media exports and innovation can help the industry grow faster.
Finally, the industry must shift toward innovation rather than only outsourcing. Developing original content, simulation tools, and research driven projects will allow Bangladesh to move from a service based industry to a knowledge based creative economy.
In conclusion, simulation in the CG industry of Bangladesh is at an early but promising stage. While studios are already using basic simulation techniques, there are clear gaps in technology, expertise, and research integration. By investing in education, infrastructure, and collaboration, Bangladesh can significantly improve its position in the global computer graphics and simulation industry.

References
DFX Studio Bangladesh CGI and fluid simulation services
Firedrum Studios VFX and animation industry in Bangladesh
Bangladeshi animation industry overview
Bangladesh computational fluid dynamics market trends
University of Dhaka fluid simulation research group
Nafees Bin Zafar contribution to fluid simulation in CG



S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/

7

Fluid dynamics. / Fluid Dynamics in Computer Graphics: A Brief Overview

« on: April 15, 2026, 03:17:52 AM »

Fluid dynamics plays an essential role in the computer graphics industry, particularly in the creation of realistic visual effects for films, video games, animation, and virtual environments. In this context, fluid dynamics refers to the simulation of natural fluid behavior such as water flow, smoke, fire, clouds, and explosions using mathematical models and computational techniques. Unlike traditional engineering applications where accuracy and physical validation are the primary goals, fluid dynamics in computer graphics focuses on visual realism, computational efficiency, and artistic control.
At the core of fluid simulation in computer graphics are the Navier–Stokes equations, which describe how fluids move under forces such as pressure, viscosity, and external influences. However, directly solving these equations in real time is computationally expensive, especially for high resolution scenes. As a result, the CG industry often uses simplified or approximate models that balance realism with performance. Grid based methods such as Eulerian approaches and particle based methods such as Lagrangian approaches are widely used to simulate different types of fluids. For example, smoke and fire are typically simulated using grid based solvers, while water splashes and droplets are often modeled using particle systems like Smoothed Particle Hydrodynamics.
Simulation tools such as Houdini, Blender, Maya, and RealFlow have integrated fluid solvers that allow artists to create highly detailed and controllable fluid effects. These tools provide parameters for controlling velocity, turbulence, viscosity, and interaction with objects, enabling the creation of visually compelling scenes. In film production, fluid simulations are used extensively to create realistic ocean waves, explosions, and atmospheric effects. In video games, real time fluid simulation is more challenging due to hardware limitations, so developers often use optimized or precomputed techniques to achieve believable effects without heavy computation.
In recent years, Artificial Intelligence has started to influence fluid dynamics in computer graphics. Machine learning models are being used to accelerate simulations, predict fluid behavior, and enhance visual quality. For instance, neural networks can be trained to generate high resolution fluid details from low resolution simulations, significantly reducing computational cost. AI based methods also help in denoising simulation outputs and improving temporal consistency in animations. This allows studios to produce high quality effects faster and at lower cost.
Despite these advancements, there are still several limitations in fluid dynamics within the CG industry. One major challenge is achieving a balance between realism and performance, especially in real time applications like gaming and virtual reality. High fidelity simulations require significant computational resources, which are not always available. Another limitation is the difficulty in controlling fluid behavior precisely, as small changes in parameters can lead to unpredictable results. Additionally, while AI methods are promising, they often lack physical interpretability and may produce visually plausible but physically inaccurate results.
In conclusion, fluid dynamics is a critical component of modern computer graphics, enabling the creation of realistic and immersive visual effects. Through the use of computational models, simulation tools, and emerging AI techniques, the CG industry continues to push the boundaries of visual realism. However, challenges related to computation, control, and physical accuracy remain important areas for future development.
References include Bridson’s Fluid Simulation for Computer Graphics, Stam’s Stable Fluids method published in SIGGRAPH, Müller and colleagues’ work on particle based fluid simulation, and recent research on AI driven fluid simulation in computer graphics from journals such as ACM Transactions on Graphics and IEEE Transactions on Visualization and Computer Graphics.

8

Particle dynamic expert. / Particle Dynamics and the Transformative Role of Artificial Intelligence

« on: April 15, 2026, 03:14:55 AM »

Particle dynamics is a fundamental branch of physics that studies the motion of particles under the influence of forces. A particle is considered as an object having mass but negligible size, which allows physicists to simplify real world problems by ignoring shape and rotational effects. The principles of particle dynamics are primarily based on Newton’s laws of motion, especially the second law which states that force is equal to the product of mass and acceleration. This relationship forms the basis for analyzing how objects move in response to different forces such as gravity, friction, and tension. The subject plays a crucial role in understanding natural phenomena and engineering systems, including planetary motion, fluid behavior, and mechanical design.
In classical particle dynamics, motion is described through concepts such as velocity, acceleration, momentum, and energy. Kinematics explains how particles move without considering the forces acting on them, while kinetics focuses on the relationship between motion and the forces that cause it. When dealing with systems involving many particles, the interactions become highly complex and often require advanced mathematical and computational techniques. Traditional methods rely heavily on solving differential equations, which can become extremely difficult or even impossible for large scale or nonlinear systems. As a result, researchers have long faced challenges related to computational cost, system complexity, and sensitivity to initial conditions.
In recent years, Artificial Intelligence has emerged as a powerful tool that enhances the study of particle dynamics. AI introduces data driven approaches that can complement traditional physics based methods. Instead of solving equations step by step, machine learning models can learn patterns from existing data and predict the behavior of particles with remarkable accuracy. This is particularly useful in systems where the governing equations are too complex or not fully understood. For example, neural networks can approximate particle trajectories or estimate forces in systems such as turbulent fluids or plasma environments.
Another important contribution of AI is its ability to accelerate simulations. In molecular dynamics, where interactions between atoms and molecules are studied, AI models can replace computationally expensive calculations with faster approximations while maintaining a high level of accuracy. This allows scientists to simulate larger systems over longer periods of time, which was previously impractical. AI also helps in identifying hidden patterns and relationships in complex datasets, leading to new insights in areas such as astrophysics, climate science, and materials engineering.
Furthermore, AI plays a significant role in optimization and control of dynamic systems. It can be used to design efficient engineering processes, improve the performance of particle accelerators, and control systems involving multiple moving agents. In advanced fields such as quantum mechanics and materials science, AI assists in predicting molecular structures, modeling atomic interactions, and accelerating the discovery of new materials and drugs.
Despite its advantages, the use of AI in particle dynamics also has limitations. It requires large amounts of high quality data for training and may produce results that are difficult to interpret due to its black box nature. Moreover, AI models must be carefully validated to ensure that their predictions are consistent with established physical laws. Therefore, AI is best viewed as a complementary tool rather than a replacement for traditional theoretical approaches.
In conclusion, particle dynamics remains a vital area of physics that provides deep insight into the motion of objects under various forces. The integration of Artificial Intelligence has significantly expanded the capabilities of researchers by enabling faster simulations, improved predictions, and better handling of complex systems. As technology continues to advance, the combination of classical physics and modern AI techniques is expected to drive further innovation and discovery in science and engineering.
References include Goldstein, Poole, and Safko in Classical Mechanics, Landau and Lifshitz in Mechanics, Frenkel and Smit in Understanding Molecular Simulation, Karniadakis and colleagues in Nature Reviews Physics on physics informed machine learning, Noé and others in Annual Review of Physical Chemistry on machine learning for molecular simulation, and Brunton and Kutz in Data Driven Science and Engineering.


References
Goldstein, H., Poole, C., & Safko, J. (2002). Classical Mechanics (3rd ed.). Addison-Wesley.
Landau, L. D., & Lifshitz, E. M. (1976). Mechanics (3rd ed.). Pergamon Press.
Frenkel, D., & Smit, B. (2001). Understanding Molecular Simulation. Academic Press.
Karniadakis, G. E., et al. (2021). “Physics-informed machine learning.” Nature Reviews Physics, 3, 422–440.
Noé, F., et al. (2020). “Machine learning for molecular simulation.” Annual Review of Physical Chemistry, 71, 361–390.
Brunton, S. L., & Kutz, J. N. (2019). Data-Driven Science and Engineering. Cambridge University Press.



S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/

9

Faculty Forum / Popular Agentic AI Tools and How to Use Them Locally

« on: April 15, 2026, 03:10:54 AM »

1. Auto-GPT
What it is:
•   One of the first agentic AI systems
•   Can plan and execute tasks automatically
What it can do:
•   Build apps
•   Research topics
•   Write and run code
•   Automate workflows
How to use on your PC:
•   Install Python
•   Download Auto-GPT from GitHub
•   Add your OpenAI API key
•   Run it using command prompt
Best for: Developers and advanced users

2. CrewAI
What it is:
•   Multi-agent system (team of AI agents working together)
What it can do:
•   Assign roles (e.g., researcher, coder, writer)
•   Complete complex tasks step by step
How to use locally:
•   Install Python
•   Install CrewAI using pip
•   Write a simple script to define agents
•   Run from terminal
Best for: Structured projects and teamwork-like AI

3. LangChain Agents
What it is:
•   Framework to build AI agents
•   Connects AI with tools, APIs, and data
What it can do:
•   Build chatbots
•   Automate tasks
•   Connect with databases and apps
How to use locally:
•   Install Python
•   Install LangChain library
•   Use Jupyter Notebook or VS Code
•   Connect with an AI model (OpenAI or local model)
 Best for: Custom AI applications

4. Ollama (Local AI Runner)
What it is:
•   Runs AI models directly on your PC (no cloud needed)
What it can do:
•   Run LLMs like Llama, Mistral locally
•   Build private AI assistants
How to use locally:
•   Download Ollama from official site
•   Install it
•   Run commands like:
ollama run llama3
Best for: Privacy and offline use

5. LM Studio
What it is:
•   Easy interface to run local AI models
What it can do:
•   Chat with AI offline
•   Run models without coding
How to use locally:
•   Download LM Studio
•   Install and open
•   Download a model
•   Start chatting
Best for: Beginners

6. Devin (Concept / Advanced AI Agent)
What it is:
•   AI software engineer (still evolving)
What it can do:
•   Write code
•   Debug
•   Build apps automatically
How to use locally:
•   Not fully available locally yet
•   But similar setups can be done using:
o   Auto-GPT
o   LangChain
Best for: Future autonomous development

7. ChatGPT + Tools (Semi-Agentic)
What it is:
•   AI assistant with agent-like capabilities
What it can do:
•   Write code
•   Plan projects
•   Help build apps step by step
How to use on PC:
•   Use browser or desktop app
•   Combine with:
o   VS Code
o   GitHub Copilot
Best for: Everyday users

Simple Setup You Need on Your PC
To use most agentic AI tools, you need:
•   A laptop or desktop
•   Internet connection (for cloud tools)
•   Python installed
•   Basic tools like:
o   VS Code
o   Terminal / Command Prompt
Optional but helpful:
•   GPU (for faster local AI)
•   Git (for downloading tools)

Easy Example Workflow
You want to build a website:
1.   Use ChatGPT → plan the idea
2.   Use GitHub Copilot → write code
3.   Use Auto-GPT → automate tasks
4.   Use browser → test your site
You just guide AI, it does most of the work

Important Tips
•   Start simple, don’t use everything at once
•   Learn basic commands (Python, terminal)
•   Always check AI output
•   Use local tools (Ollama, LM Studio) for privacy
•   Use cloud tools for more power

Simple Conclusion
Agentic AI tools are like smart assistants that can build things for you.
With just a laptop, you can:
•   Create apps
•   Build websites
•   Automate tasks
The key is:
Give clear instructions and guide the AI step by step



S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/

10

Faculty Forum / How to Work with Agentic AI and Build Projects Easily on Your Computer

« on: April 15, 2026, 03:05:54 AM »

What is Agentic AI?
•   Agentic AI means AI that can think, plan, and act to complete tasks.
•   It does not just follow commands, it can break a goal into steps and finish it.
•   Example: You say “build a website” → AI can write code, design, and fix errors.

Why It is Useful
•   You don’t need to be an expert coder
•   It saves time and effort
•   Helps you build apps, websites, designs, videos, etc.
•   Makes technology accessible to everyone

How Anyone Can Start Using It
1. Have a Clear Goal
•   Decide what you want to build
•   Example:
o   A website
o   A mobile app
o   A portfolio
•   Clear goal = better results from AI

2. Use Simple Tools
You can start with:
•   ChatGPT or similar AI tools
•   GitHub Copilot (for coding)
•   Canva AI (for design)
•   No-code tools (like Wix, Webflow)
These tools work on normal laptops or PCs

3. Give Instructions in Simple Language
•   You don’t need technical words
•   Just explain like talking to a human
Example:
•   “Create a simple website with a homepage and contact form”
•   “Make a to-do list app in Python”

4. Let AI Break Down the Work
•   Agentic AI can:
o   Plan steps
o   Write code
o   Suggest designs
o   Fix mistakes
•   You just guide it step by step

5. Check and Improve the Output
•   Always review what AI gives
•   Ask it to improve:
o   “Make it better”
o   “Fix errors”
o   “Add more features”
 Human checking is very important


6. Learn While Building
•   You don’t need to know everything first
•   Learn by doing
•   Ask AI:
o   “Explain this code”
o   “How does this work?”

7. Use Internet and Cloud
•   Most AI tools work online
•   You don’t need a powerful computer
•   Just need:
o   Internet connection
o   Basic laptop or PC

What You Can Build Easily
•   Websites
•   Apps
•   Games
•   Designs
•   Videos
•   AI tools

Important Things to Remember
•   AI can make mistakes → always check
•   Don’t depend 100% on AI
•   Keep learning basic concepts
•   Use AI as a helper, not a replacement

Future of Agentic AI
•   AI will become more powerful
•   You may be able to build full projects with just ideas
•   Anyone can become a creator, not just a user

Simple Conclusion
•   Agentic AI makes development easy and fast
•   Anyone with a laptop can build things
•   You just need:
o   Clear idea
o   Simple instructions
o   Basic understanding
It is like having a smart assistant that helps you create anything

References
Russell, S., and Norvig, P. Artificial Intelligence: A Modern Approach, 2021
OpenAI. Advances in Agentic AI Systems, 2024–2025
GitHub. Copilot and AI Coding Tools Documentation, 2023–2025
Goodfellow, I., Bengio, Y., Courville, A. Deep Learning, MIT Press, 2016




S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/

11

Faculty Forum / How Did the Bangla New Year Begin?

« on: April 15, 2026, 02:59:11 AM »

The celebration of the Bangla New Year, known as Pohela Boishakh, is deeply rooted in the historical, cultural, and agrarian traditions of Bengal. Far from being merely a festive occasion, it represents a synthesis of economic necessity, imperial administration, and cultural evolution that dates back several centuries.
The origin of the Bangla calendar, and thus the Bangla New Year, is closely associated with the Mughal period, particularly during the reign of Emperor Akbar in the sixteenth century. At that time, the Mughal administration faced a practical problem in collecting land revenue. The Islamic Hijri calendar, which was based on lunar cycles, did not align with the agricultural seasons of Bengal. Since taxes were primarily collected from farmers, this mismatch created difficulties, as harvest times did not correspond with tax collection periods. To resolve this issue, Emperor Akbar introduced a reformed calendar known as the Fasli San around 1556 CE, which combined elements of the Islamic lunar calendar with the traditional solar calendar used in the region (Eaton, 1993; Ahmed, 1968).
This newly structured calendar was aligned with the agricultural cycle, making it easier for farmers to pay taxes after the harvest. The first day of this calendar came to be recognized as the beginning of the Bangla year. Over time, this administrative reform gradually transformed into a cultural tradition. Landlords would open new accounting books on this day in a practice known as Haal Khata, inviting tenants and customers to settle previous dues and start afresh. This economic ritual contributed significantly to embedding the day into the social fabric of Bengal.
As centuries passed, Pohela Boishakh evolved beyond its administrative origins into a broader cultural celebration. During the colonial period and particularly in the twentieth century, the festival gained a new dimension as a symbol of Bengali identity. In East Bengal, which later became Bangladesh, the celebration of the Bangla New Year became closely linked with cultural expression, language, and nationalism. The rise of the Bengali cultural movement, especially during the Language Movement of 1952 and the subsequent struggle for independence, further strengthened the importance of Pohela Boishakh as a marker of secular and cultural unity (Ahmed, 2001).
In modern Bangladesh, Pohela Boishakh is celebrated on 14 April according to the reformed Bangla calendar introduced by the Bangla Academy in 1987, which standardized the calendar for consistency. The day is marked by vibrant cultural activities, including processions such as the Mangal Shobhajatra, traditional music, fairs, and communal gatherings. These practices reflect not only joy and renewal but also a collective affirmation of cultural heritage. The recognition of Mangal Shobhajatra by UNESCO as an Intangible Cultural Heritage of Humanity further highlights the global significance of this tradition.
Thus, the Bangla New Year is not simply a chronological marker but a historical continuum that connects agrarian practices, Mughal administrative reforms, and modern cultural identity. Its evolution demonstrates how a practical solution to a fiscal problem gradually transformed into one of the most important cultural celebrations of the Bengali people.

References
Ahmed, S. (1968). The Mughal Administration in Bengal. Oxford University Press.
Ahmed, R. (2001). The Bengali Identity and Cultural Nationalism. University Press Limited.
Eaton, R. M. (1993). The Rise of Islam and the Bengal Frontier, 1204–1760. University of California Press.
Bangla Academy (1987). Reformation of the Bangla Calendar. Dhaka.
UNESCO (2016). Mangal Shobhajatra on Pahela Baishakh – Intangible Cultural Heritage of Humanity.




S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/

12

Cinematography. / Cinematography and Tourism in Bangladesh

« on: April 15, 2026, 02:55:06 AM »

Cinematography, the art of capturing visual images through camera techniques, lighting, framing, and movement, plays a crucial role in shaping how places are perceived by audiences. In the modern era of digital media, where visual content dominates platforms such as film, television, YouTube, and social media, cinematography has become a powerful tool for promoting tourism. For a country like Bangladesh, which possesses rich natural landscapes, cultural heritage, and diverse environments, cinematography holds significant potential to influence tourism development and global perception.
Bangladesh is geographically and culturally diverse, offering locations such as the Sundarbans, the world’s largest mangrove forest, and Cox’s Bazar, the longest natural sea beach in the world. These locations provide visually compelling settings that are highly suitable for cinematic representation . Cinematography has the ability to transform these real places into emotionally engaging visual experiences. When landscapes are presented through carefully composed shots, lighting, and storytelling, they become more attractive and memorable to viewers. This visual appeal directly influences tourism by inspiring audiences to visit the locations they see on screen.
Globally, the concept of film induced tourism has proven that movies and visual media can significantly increase tourist arrivals to specific destinations. In the context of Bangladesh, although this phenomenon is still developing, there are clear signs of its potential. The country has recently experienced an increase in foreign film permit applications, indicating growing international interest in Bangladesh as a filming destination . This trend suggests that cinematography is beginning to position Bangladesh within the global visual media landscape. When international productions showcase Bangladeshi locations, they contribute to global awareness and can attract foreign tourists.
The importance of cinematography in tourism is also closely linked to the economic impact of the tourism sector. Tourism contributes to employment and national income, supporting millions of jobs in Bangladesh and contributing around three percent to GDP . However, despite this potential, the number of international tourists remains relatively low compared to the country’s population . One of the reasons for this gap is the lack of effective global marketing and visual representation. Cinematography can address this issue by creating compelling visual narratives that promote Bangladesh as a desirable destination.
In the era of social media and digital content, cinematography is no longer limited to film industries. Travel vloggers, drone filmmakers, and content creators are now playing a significant role in tourism promotion. High quality cinematic visuals shared on platforms such as YouTube and Instagram can reach global audiences instantly. Bangladesh’s vibrant urban scenes, such as Old Dhaka, and its natural environments, such as Sylhet’s tea gardens and the Chittagong Hill Tracts, provide unique visual content that can attract tourists when presented effectively. Cinematic storytelling helps create emotional connections, making viewers more likely to associate the destination with beauty, culture, and authenticity.
However, the full potential of cinematography in promoting tourism in Bangladesh is not yet fully realized. The local film industry has faced structural challenges, including declining infrastructure, reduced production capacity, and limited investment . These limitations affect the ability to produce high quality visual content that can compete on a global scale. Additionally, there is a lack of coordinated efforts between the tourism sector and the media industry, which reduces the effectiveness of visual promotion strategies.
Another important factor is the need for professional development in cinematography. Skilled cinematographers, such as those recognized at national film awards, demonstrate that high quality visual storytelling is possible within Bangladesh . However, expanding this expertise and integrating it with tourism initiatives is essential for long term growth. Investment in training, technology, and international collaboration can help improve the quality of visual content and strengthen the connection between cinematography and tourism.
Looking toward the future, cinematography is expected to play an even more significant role in tourism development in Bangladesh. With advancements in digital cameras, drones, and AI assisted editing tools, creating high quality cinematic content is becoming more accessible. Government initiatives, such as tourism master plans and digital promotion strategies, indicate a growing recognition of the importance of visual media in attracting tourists . If effectively implemented, these efforts can position Bangladesh as a visually compelling destination in the global tourism market.
In conclusion, cinematography is not merely an artistic practice but a strategic tool for tourism development in the modern media landscape. In Bangladesh, where natural beauty and cultural richness are abundant but underrepresented globally, cinematography can bridge the gap between potential and perception. By enhancing visual storytelling, promoting locations through film and digital media, and investing in creative industries, Bangladesh can leverage cinematography to strengthen its tourism sector and improve its global image.




S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/

13

Pre-production. / Preproduction Made Simple in the Age of AI

« on: April 15, 2026, 02:51:55 AM »

Preproduction is one of the most critical phases in any creative project, particularly in film, animation, advertising, and digital media. It is the stage where ideas are transformed into structured plans before actual production begins. Traditionally, preproduction involves script development, storyboarding, budgeting, scheduling, casting, location planning, and visual design. This phase determines the efficiency, cost, and overall success of a project. A well planned preproduction process reduces uncertainty, minimizes errors, and ensures that creative vision aligns with practical execution. Without proper preproduction, even the most compelling ideas can fail during production due to lack of organization and clarity.
Understanding preproduction is essential for anyone involved in creative work, not only professionals but also students, independent creators, and emerging filmmakers. It provides a framework for thinking systematically about a project, breaking it down into manageable components, and anticipating potential challenges. For example, script breakdown allows creators to identify required resources such as props, locations, and actors, while storyboarding helps visualize scenes and camera movements before filming begins. Budgeting and scheduling ensure that resources are allocated efficiently and deadlines are met. In essence, preproduction is where creativity meets planning, turning abstract ideas into actionable steps.
In the past, preproduction required significant time, expertise, and collaboration among multiple departments. It often involved manual processes, extensive documentation, and iterative revisions. However, the emergence of artificial intelligence has begun to simplify and transform this stage, making it more accessible and efficient. AI powered tools can now assist in script analysis, automatically breaking down scenes and identifying key elements such as characters, locations, and props. This reduces the time required for initial planning and helps creators quickly understand the scope of their projects.
AI is also revolutionizing storyboarding and visual development. With generative models, creators can generate visual representations of scenes directly from text descriptions, allowing them to explore different visual styles and compositions without requiring advanced drawing skills. This is particularly beneficial for independent creators who may not have access to professional storyboard artists. Similarly, AI driven tools can assist in location scouting by analyzing visual references and suggesting suitable environments, either real or virtual.
Another important aspect of preproduction is scheduling and resource management, which can be complex and time consuming. AI systems can optimize production schedules by analyzing constraints such as availability of actors, locations, and equipment. These systems can generate efficient timelines, identify potential conflicts, and suggest adjustments in real time. Budgeting can also be supported by AI through predictive analysis, helping creators estimate costs more accurately and avoid overspending.
Perhaps the most transformative impact of AI is the democratization of preproduction. Tasks that once required specialized knowledge and large teams can now be managed by individuals or small groups using AI assisted tools. This lowers the barrier to entry for creative production, enabling more people to bring their ideas to life. A single creator can now write a script, generate storyboards, plan schedules, and visualize scenes with the support of AI, significantly reducing both time and cost.
Despite these advantages, it is important to recognize that AI does not replace the need for human judgment and creativity. Preproduction still requires critical decision making, artistic vision, and contextual understanding that AI alone cannot fully replicate. Instead, AI should be seen as a supportive tool that enhances human capability, allowing creators to focus more on storytelling and less on repetitive or technical tasks.
Looking ahead, the role of AI in preproduction is likely to expand further, integrating more deeply into creative workflows. Future systems may act as intelligent assistants capable of managing entire preproduction pipelines, from concept development to detailed planning. This will not only improve efficiency but also enable new forms of creative experimentation, where ideas can be tested and refined rapidly before production begins.
In conclusion, preproduction remains the foundation of any successful creative project, providing the structure and clarity needed to execute ideas effectively. With the integration of artificial intelligence, this process is becoming more efficient, accessible, and flexible. While the core principles of planning and organization remain unchanged, the tools and methods are evolving, empowering a new generation of creators to manage preproduction with greater ease and confidence.

References
Field, S. 2005. Screenplay: The Foundations of Screenwriting. Delta.
Rabiger, M. 2013. Directing: Film Techniques and Aesthetics. Focal Press.
Goodfellow, I., Bengio, Y., and Courville, A. 2016. Deep Learning. MIT Press.
Autodesk. 2023 to 2025. AI Tools in Media and Entertainment Production.
Adobe Inc. 2023 to 2025. AI in Creative Cloud and Production Workflows.
SIGGRAPH Proceedings. 2023 to 2025. AI in Film Production and Previsualization.


S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/

14

Script Writing. / Scriptwriting in the Age of Artificial Intelligence

« on: April 15, 2026, 02:48:48 AM »

Scriptwriting, as a foundational element of storytelling in film, television, theatre, and digital media, has traditionally been a deeply human endeavor shaped by imagination, cultural context, and emotional intelligence. Writers craft narratives by drawing on lived experiences, social observations, and artistic intuition, constructing characters and dialogues that resonate with audiences. However, the emergence of artificial intelligence is beginning to reshape this creative landscape, introducing new tools, workflows, and possibilities that challenge conventional notions of authorship and creativity.
One of the most immediate impacts of AI on scriptwriting is the enhancement of the writing process through intelligent assistance. AI-powered tools can now generate story ideas, suggest plot structures, assist with dialogue writing, and even provide alternative narrative directions. These systems are trained on large corpora of scripts and literary works, allowing them to recognize patterns in storytelling, such as common character arcs, genre conventions, and pacing techniques. As a result, writers can use AI as a collaborative partner, particularly in the early stages of ideation, where overcoming creative blocks is often a major challenge. Rather than replacing the writer, AI serves as a catalyst for creativity, expanding the range of possibilities that can be explored.
In addition to idea generation, AI is increasingly being used for script analysis and refinement. Advanced algorithms can evaluate scripts based on factors such as narrative coherence, emotional tone, character development, and audience engagement. For example, AI systems can predict how audiences might respond to certain plot elements or identify inconsistencies within a storyline. This data driven approach introduces a new layer of objectivity into what has traditionally been a subjective process. While such tools can improve efficiency and reduce risk in commercial productions, they also raise questions about whether storytelling might become overly formulaic if driven too heavily by algorithmic optimization.
Another significant development is the emergence of generative AI models capable of producing entire scripts from prompts or outlines. These systems can generate dialogues, scenes, and even full screenplays in a matter of minutes. This capability has profound implications for content creation, particularly in industries where speed and volume are critical, such as streaming platforms and digital media. However, the quality of AI generated scripts often varies, and while they may demonstrate structural coherence, they may lack the depth, nuance, and originality that characterize compelling human storytelling. This highlights the continued importance of human writers in shaping narrative meaning and emotional resonance.
The integration of AI into scriptwriting also raises important ethical and professional considerations. One of the primary concerns is authorship and intellectual property. Since AI systems are trained on existing works, there is ongoing debate about the extent to which generated content may replicate or derive from original sources. Additionally, the increasing use of AI in writing processes has sparked concerns about job displacement within the creative industries. Writers may face pressure to adapt to new workflows or risk being replaced in certain contexts, particularly in lower budget or high volume content production.
Despite these challenges, AI also opens new creative frontiers for scriptwriting. Interactive storytelling, for instance, is becoming more feasible through AI driven systems that can adapt narratives in real time based on user input. This is particularly relevant in gaming, virtual reality, and emerging media formats, where stories are no longer linear but dynamic and participatory. AI can also enable multilingual scriptwriting and translation, making stories more accessible to global audiences and facilitating cross cultural exchange.
Looking toward the future, the relationship between AI and scriptwriting is likely to evolve into a collaborative model where human creativity and machine intelligence complement each other. Writers will increasingly take on the role of narrative architects, defining themes, tone, and direction, while AI systems assist with execution, variation, and optimization. The challenge will be to maintain the authenticity and emotional depth of storytelling while leveraging the efficiency and scalability of AI technologies.
In conclusion, the impact of artificial intelligence on scriptwriting is both transformative and complex. While AI introduces powerful tools that enhance productivity, support creativity, and enable new forms of storytelling, it also challenges traditional ideas of authorship, originality, and artistic control. Rather than replacing human writers, AI is reshaping the craft, encouraging a new form of creative collaboration that blends human insight with computational capability. The future of scriptwriting will depend on how effectively this balance is achieved, ensuring that technology serves to enrich rather than diminish the art of storytelling.

References
McKee, R. 1997. Story: Substance, Structure, Style and the Principles of Screenwriting. HarperCollins.
Field, S. 2005. Screenplay: The Foundations of Screenwriting. Delta.
Goodfellow, I., Bengio, Y., and Courville, A. 2016. Deep Learning. MIT Press.
Elgammal, A., Liu, B., Elhoseiny, M., and Mazzone, M. 2017. Creative Adversarial Networks. arXiv:1706.07068.
Kaplan, A. M., and Haenlein, M. 2020. Rulers of the World, Unite! The Challenges and Opportunities of Artificial Intelligence. Business Horizons.
OpenAI. 2024 to 2025. Advances in Generative AI and Language Models.
Writers Guild of America. 2023. Reports on AI and the Future of Screenwriting.





S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit:[url] https://monowarkayser.com/[/url]

15

Augmented Reality / Why AR is Still Lagging in Bangladesh

« on: April 15, 2026, 02:46:46 AM »

Augmented reality (AR), despite its global rise as a transformative technology, remains underdeveloped and unevenly adopted in Bangladesh. While the country has shown increasing interest in digital innovation, the integration of AR into mainstream industries, education, and everyday applications is still in its early stages. This gap is not due to a lack of potential, but rather the presence of structural, technological, and socio-economic constraints that limit its widespread implementation. Understanding these limitations is essential to evaluating both the current state and the future trajectory of AR in Bangladesh.
One of the most fundamental barriers to AR adoption in Bangladesh is the limitation of technological infrastructure. Although urban areas are experiencing rapid digital growth, many rural regions still lack reliable high-speed internet and advanced computing resources. AR systems often require stable connectivity, powerful devices, and real-time data processing capabilities, which are not universally accessible across the country. Studies indicate that limited access to high-speed internet and inadequate infrastructure remain key restraints for AR integration, particularly outside major cities . This creates a digital divide, where only a small segment of the population can engage with advanced technologies, thereby restricting the scalability of AR solutions.
Closely related to infrastructure is the issue of high implementation costs. AR technologies often involve expensive hardware such as advanced smartphones, sensors, and head-mounted displays, as well as software development costs. For many educational institutions, startups, and small businesses in Bangladesh, these costs are prohibitive. Research highlights that the high initial investment required for AR systems significantly slows adoption, particularly in sectors like education where funding is limited . As a result, AR remains more of an experimental or niche technology rather than a widely deployed solution.
Another critical limitation is the lack of skilled professionals and technical expertise. The development and deployment of AR systems require interdisciplinary knowledge in computer vision, 3D modeling, user experience design, and software engineering. In Bangladesh, there is still a shortage of trained professionals in these areas, which restricts both innovation and implementation. Academic and industry studies point out that a lack of expertise and trained human resources is one of the primary challenges in adopting AR technologies in developing countries like Bangladesh . Without a strong talent pipeline, even well-funded initiatives may struggle to sustain and scale.
Awareness and understanding of AR technology also remain limited among both institutions and the general public. While AR is already present in everyday applications such as social media filters and QR-based interactions, its broader potential is not widely recognized. Many educators, business owners, and policymakers lack sufficient knowledge about how AR can be integrated into their respective fields. This lack of awareness leads to underutilization of the technology, even in cases where the necessary tools are available. Reports suggest that insufficient awareness and training significantly hinder the effective adoption of AR in sectors such as education .
In addition to these structural challenges, there are also limitations related to content and localization. Most AR applications are developed for global markets and may not reflect the cultural, linguistic, or contextual needs of Bangladesh. The absence of locally relevant content reduces the effectiveness and appeal of AR systems for Bangladeshi users. For example, educational AR applications may not align with national curricula, and commercial AR experiences may not resonate with local consumer behavior. This highlights the need for localized development, which in turn requires both investment and expertise.
Another important issue is the broader ecosystem readiness for advanced technologies. While Bangladesh is undergoing digital transformation, the readiness of institutions, particularly in education and public sectors, remains uneven. Even where technology is introduced, its integration into existing systems is often limited by outdated practices, lack of training, and resistance to change. Recent discussions on digital transformation in Bangladesh emphasize that technological success depends not only on innovation but also on institutional readiness and the ability to adapt effectively . This is particularly relevant for AR, which requires not just adoption but meaningful integration into workflows.
Despite these limitations, it is important to recognize that AR in Bangladesh is not absent but rather emerging. The technology is gradually gaining traction in areas such as education, marketing, and startup innovation. Everyday applications, including social media filters and mobile-based AR experiences, indicate that the foundation for broader adoption already exists . Moreover, increasing smartphone penetration and ongoing digital initiatives suggest that many of the current barriers may be reduced over time.
In conclusion, the lack of widespread adoption of augmented reality in Bangladesh is not due to a single factor but rather a combination of infrastructural limitations, high costs, lack of expertise, limited awareness, and insufficient ecosystem readiness. These challenges are interconnected and require coordinated efforts from government, industry, and academia to address effectively. While the potential of AR in Bangladesh is significant, its realization will depend on investments in infrastructure, education, and local innovation. As these conditions improve, AR has the potential to become a powerful tool for economic growth, education, and digital transformation in the country.




References
Bangladesh Augmented and Virtual Reality in Education Market Report, 6Wresearch.
Sharif, A. et al. Exploring the Opportunities and Challenges of Adopting Augmented Reality in Education in a Developing Country.
The Daily Star. The Reality of Augmented Reality in the Bangladeshi Context.
Goinnovior. Augmented Reality in Bangladesh: Opportunities and Challenges.
The Business Standard. Digital Transformation in Bangladesh’s Higher Education.



S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/