AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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2025
Yadav, R.; Huzooree, G.; Yadav, M.; Gangodawilage, D. S. K.
Generative AI for personalized learning content creation Book Section
In: Transformative AI Practices for Personalized Learning Strategies, pp. 107–130, IGI Global, 2025, ISBN: 979-836938746-7 (ISBN); 979-836938744-3 (ISBN).
Abstract | Links | BibTeX | Tags: Adaptive feedback, Advanced Analytics, AI systems, Contrastive Learning, Educational contents, Educational experiences, Enhanced learning, Ethical technology, Federated learning, Immersive, Learning content creation, Personalized learning, Student engagement, Students, Supervised learning, Tools and applications, Virtual Reality
@incollection{yadav_generative_2025,
title = {Generative AI for personalized learning content creation},
author = {R. Yadav and G. Huzooree and M. Yadav and D. S. K. Gangodawilage},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005387236&doi=10.4018%2f979-8-3693-8744-3.ch005&partnerID=40&md5=904e58b9c6de83dcd431c1706dda02b3},
doi = {10.4018/979-8-3693-8744-3.ch005},
isbn = {979-836938746-7 (ISBN); 979-836938744-3 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Transformative AI Practices for Personalized Learning Strategies},
pages = {107–130},
publisher = {IGI Global},
abstract = {Generative AI has emerged as a transformative force in personalized learning, offering unprecedented opportunities to tailor educational content to individual needs. By leveraging advanced algorithms and data analysis, AI systems can dynamically generate customized materials, provide adaptive feedback, and foster student engagement. This chapter explores the intersection of generative AI and personalized learning, discussing its techniques, tools, and applications in creating immersive and adaptive educational experiences. Key benefits include enhanced learning outcomes, efficiency, and scalability. However, challenges such as data privacy, algorithmic bias, and equitable access must be addressed to ensure responsible implementation. Future trends, including the integration of immersive technologies like Virtual Reality (VR) and predictive analytics, highlight AI's potential to revolutionize education. By navigating ethical considerations and fostering transparency, generative AI can become a powerful ally in creating inclusive, engaging, and student- centered learning environments. © 2025, IGI Global Scientific Publishing. All rights reserved.},
keywords = {Adaptive feedback, Advanced Analytics, AI systems, Contrastive Learning, Educational contents, Educational experiences, Enhanced learning, Ethical technology, Federated learning, Immersive, Learning content creation, Personalized learning, Student engagement, Students, Supervised learning, Tools and applications, Virtual Reality},
pubstate = {published},
tppubtype = {incollection}
}
2024
Leong, C. W.; Jawahar, N.; Basheerabad, V.; Wörtwein, T.; Emerson, A.; Sivan, G.
Combining Generative and Discriminative AI for High-Stakes Interview Practice Proceedings Article
In: ACM Int. Conf. Proc. Ser., pp. 94–96, Association for Computing Machinery, 2024, ISBN: 979-840070463-5 (ISBN).
Abstract | Links | BibTeX | Tags: AI systems, College admissions, Continuous improvements, End to end, Interactive computer graphics, Interactive dialog system, interactive dialogue systems, Language Model, Modeling languages, Multi-modal, Multimodal computing, Video interview, video interviews, Virtual avatar, Virtual environments, Virtual Reality
@inproceedings{leong_combining_2024,
title = {Combining Generative and Discriminative AI for High-Stakes Interview Practice},
author = {C. W. Leong and N. Jawahar and V. Basheerabad and T. Wörtwein and A. Emerson and G. Sivan},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211135262&doi=10.1145%2f3686215.3688377&partnerID=40&md5=4f53f4466d43840510a36c125eeefa16},
doi = {10.1145/3686215.3688377},
isbn = {979-840070463-5 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {ACM Int. Conf. Proc. Ser.},
pages = {94–96},
publisher = {Association for Computing Machinery},
abstract = {We present a demo comprising an end-to-end AI pipeline for practicing video interviews for a high-stakes scenarios (i.e., college admissions) with personalized, actionable feedback for continuous improvement of the user. This system provides personalized, actionable feedback for continuous user improvement. Utilizing large language models (LLMs), we generate questions and responses for a virtual avatar interviewer. Our focus on key qualities - such as concise responses with low latency, empathy, and smooth topic navigation - led to a comparative evaluation of several prominent LLMs, each undergoing evolutionary development. We also discuss the integration of avatar technology to create an immersive, virtual environment for naturalistic dyadic conversations. © 2024 Owner/Author.},
keywords = {AI systems, College admissions, Continuous improvements, End to end, Interactive computer graphics, Interactive dialog system, interactive dialogue systems, Language Model, Modeling languages, Multi-modal, Multimodal computing, Video interview, video interviews, Virtual avatar, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}