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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63675

GENERATIVE AI FOR SIMULATION BASED LEARNING

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63675

Volume:

10 |

5 |

May 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63675

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KIRAN K B

KIRAN K B

MASTER OF COMPUTER APPLICATION

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY

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Mentors

Dr.AMJAD KHAN

Dr.AMJAD KHAN

MASTER OF COMPUTER APPLICATION

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY

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Abstract

ABSTRACT Simulation-based learning has turned into an effective instructional strategybyofferinglearnersInteractiveand experiential environments for putting theoreticalknowledgeintopractice.Recent progress in AI further strengthened this approach,enablingmorescalable,Adaptive and Intelligent learning systems. This project looks into the use of Generative AI and Generative image and video tools into simulation-basededucationalplatformsfor thedeliveryofpersonalizedresponsiveand context-sensitive learning experiences. It investigates the use of generative technologies to generate dynamically adaptive scenarios that are realistic. conversation,artificiallygenerateddatasets and virtual learning environments that would instantly adapt to the learner Behaviour and input. this prototype sophisticated generative AI tools validate thisapproach.appliedtoaselecteddomain of training, like healthcare, customer service,orSTEMeducation. Theresultsof usability testing and feedback showed that thelearnersaremoreengaged,flexibilityin learninghad improved, and Improved with suchsystemscomparedtotraditionalstatic or rule-based simulation systems. This report presents the design framework, its implementation methodology, and the results of the evaluation, while Also, it Generative AI in educational settings. ResultsshowthatGenerativeAIhasstrong potential to reshape the futureof education in making.

KEYWORDS

Generative AI

Cite This Article

KIRAN K B, Dr.AMJAD KHAN, "GENERATIVE AI FOR SIMULATION BASED LEARNING," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-6, May 2026. https://doi.org/10.55041/IJSREM63675

Full Article/Conclusion

Future iterations will move beyond single- modalityoutputstoembraceCross-Modal Asset Generation. This involves the simultaneous, synchronized creation of high-fidelity text, spatial audio, and dynamic 3D assets. By aligning auditory cues with visual stimuli in real-time, the system can create a significantly higher degree of "presence" and sensory immersion for the learner.   Affective Computing and Bio-Feedback Integration   Stress-ResponsiveScaling:Byleveraging computer vision for facial expression analysis or wearable sensors for heart-rate variability (HRV), the system can detect "Cognitive Overload."   Adaptive Real-Time Intervention and LearnerCentricityThecoreachievementof this research is the transition from "Pre- scripted Scenarios" to Dynamic Content Orchestration. Real-Time Task Modulation: Unlike traditionalsimulationswithfixedbranching logic, the proposed system utilizes generative inference to adjust scenario complexitybasedonthelearner’sbiometric orperformance-basedtelemetry.Cognitive Load Optimization: By predicting user interaction trajectories, the system maintains the "Zone of Proximal Development,"ensuringthatchallengesare neither trivial nor cognitively overwhelming.

References

1.    Welling, M. (2014). “Auto-Encoding Variational Bayes.” 2.   Radford, A., et al. (2019). “Language Models are Unsupervised Multitask Learners.” OpenAI Technical Report.   3.   Ho, J., Jain, A., & Abbeel, P. (2020). “Denoising Diffusion Probabilistic Models.” NeurIPS.   4.   Kober, J., Bagnell, J. A., & Peters, J. (2013). “Reinforcement Learning in Robotics.” International Journal of Robotics Research. 5.   Khan, S., et al. (2020). Virtual Reality and Simulation in Education and Training. Springer. 6.  Zhang,Q.,Yang,L.T.,Chen,Z.,&Li, P.(2018).“ASurveyonDeepLearningfor Big Data.” Information Fusion. 7.     OpenAI. (2023). Generative AI for Interactive Learning Environments – Whitepaper.   8.  Pan,S.J.,&Yang,Q.(2010).“ASurvey on Transfer Learning.” Knowledge and Data Engineering.   9.   AI-Enhanced Simulation Standards for Education.

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Publication Date

5/21/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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