AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2024
Bernetti, I.; Borghini, T.; Capecchi, I.
Integrating Virtual Reality and Artificial Intelligence in Agricultural Planning: Insights from the V.AİḞ.AṘṀ. Application Proceedings Article
In: L.T., De Paolis; P., Arpaia; M., Sacco (Ed.): Lect. Notes Comput. Sci., pp. 342–350, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 03029743 (ISSN); 978-303171706-2 (ISBN).
Abstract | Links | BibTeX | Tags: Agricultural management, Agricultural planning, Agricultural resources, Artificial intelligence technologies, Collaborative Virtual Reality, Critical thinking, Educational approach, Management applications, Openai, Resource management
@inproceedings{bernetti_integrating_2024,
title = {Integrating Virtual Reality and Artificial Intelligence in Agricultural Planning: Insights from the V.AİḞ.AṘṀ. Application},
author = {I. Bernetti and T. Borghini and I. Capecchi},
editor = {De Paolis L.T. and Arpaia P. and Sacco M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204516778&doi=10.1007%2f978-3-031-71707-9_28&partnerID=40&md5=a887379b08dc925667f255cfcacfb4b9},
doi = {10.1007/978-3-031-71707-9_28},
isbn = {03029743 (ISSN); 978-303171706-2 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15027 LNCS},
pages = {342–350},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The V.A.I.F.A.R.M. (Virtual and Artificial Intelligence for Farming and Agricultural Resource Management) app explores the integration of collaborative virtual reality (VR) with generative artificial intelligence (AI), specifically utilizing ChatGPT, to enhance educational approaches within agricultural management and planning. This study aims to investigate the educational outcomes associated with the combined use of VR and AI technologies, with a particular focus on their impact on critical thinking, problem-solving abilities, and collaborative learning among university students engaged in agricultural studies. By employing VR, the project creates a simulated agricultural environment where students are tasked with various management and planning activities, offering a practical application of theoretical knowledge. The addition of ChatGPT facilitates interactive, AI-mediated dialogues, challenging students to tackle complex agricultural problems through informed decision-making processes. The research anticipates findings that suggest an improvement in student engagement and a better grasp of complicated agricultural concepts, attributed to the immersive and interactive nature of the learning experience. Furthermore, it examines the role of VR and AI in cultivating essential soft skills critical for the agricultural sector. The study contributes to the understanding of how collaborative VR and generative AI can be effectively combined to advance educational practices in agriculture, aiming for a balanced evaluation of their potential benefits without overstating the outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Agricultural management, Agricultural planning, Agricultural resources, Artificial intelligence technologies, Collaborative Virtual Reality, Critical thinking, Educational approach, Management applications, Openai, Resource management},
pubstate = {published},
tppubtype = {inproceedings}
}
The V.A.I.F.A.R.M. (Virtual and Artificial Intelligence for Farming and Agricultural Resource Management) app explores the integration of collaborative virtual reality (VR) with generative artificial intelligence (AI), specifically utilizing ChatGPT, to enhance educational approaches within agricultural management and planning. This study aims to investigate the educational outcomes associated with the combined use of VR and AI technologies, with a particular focus on their impact on critical thinking, problem-solving abilities, and collaborative learning among university students engaged in agricultural studies. By employing VR, the project creates a simulated agricultural environment where students are tasked with various management and planning activities, offering a practical application of theoretical knowledge. The addition of ChatGPT facilitates interactive, AI-mediated dialogues, challenging students to tackle complex agricultural problems through informed decision-making processes. The research anticipates findings that suggest an improvement in student engagement and a better grasp of complicated agricultural concepts, attributed to the immersive and interactive nature of the learning experience. Furthermore, it examines the role of VR and AI in cultivating essential soft skills critical for the agricultural sector. The study contributes to the understanding of how collaborative VR and generative AI can be effectively combined to advance educational practices in agriculture, aiming for a balanced evaluation of their potential benefits without overstating the outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Liu, M.; M'Hiri, F.
Beyond Traditional Teaching: Large Language Models as Simulated Teaching Assistants in Computer Science Proceedings Article
In: SIGCSE - Proc. ACM Tech. Symp. Comput. Sci. Educ., pp. 743–749, Association for Computing Machinery, Inc, 2024, ISBN: 979-840070423-9 (ISBN).
Abstract | Links | BibTeX | Tags: Adaptive teaching, ChatGPT, Computational Linguistics, CS education, E-Learning, Education computing, Engineering education, GPT, Language Model, LLM, machine learning, Machine-learning, Novice programmer, novice programmers, Openai, Programming, Python, Students, Teaching, Virtual Reality
@inproceedings{liu_beyond_2024,
title = {Beyond Traditional Teaching: Large Language Models as Simulated Teaching Assistants in Computer Science},
author = {M. Liu and F. M'Hiri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189289344&doi=10.1145%2f3626252.3630789&partnerID=40&md5=44ec79c8f005f4551c820c61f5b5d435},
doi = {10.1145/3626252.3630789},
isbn = {979-840070423-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {SIGCSE - Proc. ACM Tech. Symp. Comput. Sci. Educ.},
volume = {1},
pages = {743–749},
publisher = {Association for Computing Machinery, Inc},
abstract = {As the prominence of Large Language Models (LLMs) grows in various sectors, their potential in education warrants exploration. In this study, we investigate the feasibility of employing GPT-3.5 from OpenAI, as an LLM teaching assistant (TA) or a virtual TA in computer science (CS) courses. The objective is to enhance the accessibility of CS education while maintaining academic integrity by refraining from providing direct solutions to current-semester assignments. Targeting Foundations of Programming (COMP202), an undergraduate course that introduces students to programming with Python, we have developed a virtual TA using the LangChain framework, known for integrating language models with diverse data sources and environments. The virtual TA assists students with their code and clarifies complex concepts. For homework questions, it is designed to guide students with hints rather than giving out direct solutions. We assessed its performance first through a qualitative evaluation, then a survey-based comparative analysis, using a mix of questions commonly asked on the COMP202 discussion board and questions created by the authors. Our preliminary results indicate that the virtual TA outperforms human TAs on clarity and engagement, matching them on accuracy when the question is non-assignment-specific, for which human TAs still proved more reliable. These findings suggest that while virtual TAs, leveraging the capabilities of LLMs, hold great promise towards making CS education experience more accessible and engaging, their optimal use necessitates human supervision. We conclude by identifying several directions that could be explored in future implementations. © 2024 ACM.},
keywords = {Adaptive teaching, ChatGPT, Computational Linguistics, CS education, E-Learning, Education computing, Engineering education, GPT, Language Model, LLM, machine learning, Machine-learning, Novice programmer, novice programmers, Openai, Programming, Python, Students, Teaching, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
As the prominence of Large Language Models (LLMs) grows in various sectors, their potential in education warrants exploration. In this study, we investigate the feasibility of employing GPT-3.5 from OpenAI, as an LLM teaching assistant (TA) or a virtual TA in computer science (CS) courses. The objective is to enhance the accessibility of CS education while maintaining academic integrity by refraining from providing direct solutions to current-semester assignments. Targeting Foundations of Programming (COMP202), an undergraduate course that introduces students to programming with Python, we have developed a virtual TA using the LangChain framework, known for integrating language models with diverse data sources and environments. The virtual TA assists students with their code and clarifies complex concepts. For homework questions, it is designed to guide students with hints rather than giving out direct solutions. We assessed its performance first through a qualitative evaluation, then a survey-based comparative analysis, using a mix of questions commonly asked on the COMP202 discussion board and questions created by the authors. Our preliminary results indicate that the virtual TA outperforms human TAs on clarity and engagement, matching them on accuracy when the question is non-assignment-specific, for which human TAs still proved more reliable. These findings suggest that while virtual TAs, leveraging the capabilities of LLMs, hold great promise towards making CS education experience more accessible and engaging, their optimal use necessitates human supervision. We conclude by identifying several directions that could be explored in future implementations. © 2024 ACM.