Exploration of large language models in education: automated question and answer generation system and gamified learning environment
This project focuses on the development of a personalized and gamified learning environment powered by the automatic generation of questions and answers (Q&A-AG) using Large Language Models (LLMs). The system utilizes the Retrieval-Augmented Generation (RAG) process to analyze and extract knowle...
- Autores:
-
Duarte Mantilla, Ernesto José
Klopstock Triana, Nicolás
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2025
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/75407
- Acceso en línea:
- https://hdl.handle.net/1992/75407
- Palabra clave:
- Large Language Models
Gamification
Learning Environment
Retrieval-Augmented Generation
Intelligent Agent
Multi-Agent System
Question & Answer Automatic Generation
Graph-orchestrated Agents
Ingeniería
- Rights
- embargoedAccess
- License
- Attribution-NoDerivatives 4.0 International
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Exploration of large language models in education: automated question and answer generation system and gamified learning environment |
| title |
Exploration of large language models in education: automated question and answer generation system and gamified learning environment |
| spellingShingle |
Exploration of large language models in education: automated question and answer generation system and gamified learning environment Large Language Models Gamification Learning Environment Retrieval-Augmented Generation Intelligent Agent Multi-Agent System Question & Answer Automatic Generation Graph-orchestrated Agents Ingeniería |
| title_short |
Exploration of large language models in education: automated question and answer generation system and gamified learning environment |
| title_full |
Exploration of large language models in education: automated question and answer generation system and gamified learning environment |
| title_fullStr |
Exploration of large language models in education: automated question and answer generation system and gamified learning environment |
| title_full_unstemmed |
Exploration of large language models in education: automated question and answer generation system and gamified learning environment |
| title_sort |
Exploration of large language models in education: automated question and answer generation system and gamified learning environment |
| dc.creator.fl_str_mv |
Duarte Mantilla, Ernesto José Klopstock Triana, Nicolás |
| dc.contributor.advisor.none.fl_str_mv |
Manrique Piramanrique, Rubén Francisco |
| dc.contributor.author.none.fl_str_mv |
Duarte Mantilla, Ernesto José Klopstock Triana, Nicolás |
| dc.contributor.researchgroup.none.fl_str_mv |
Facultad de Ingeniería::TICSw: Tecnologías de Información y Construcción de Software |
| dc.subject.keyword.eng.fl_str_mv |
Large Language Models Gamification Learning Environment Retrieval-Augmented Generation Intelligent Agent Multi-Agent System Question & Answer Automatic Generation Graph-orchestrated Agents |
| topic |
Large Language Models Gamification Learning Environment Retrieval-Augmented Generation Intelligent Agent Multi-Agent System Question & Answer Automatic Generation Graph-orchestrated Agents Ingeniería |
| dc.subject.themes.spa.fl_str_mv |
Ingeniería |
| description |
This project focuses on the development of a personalized and gamified learning environment powered by the automatic generation of questions and answers (Q&A-AG) using Large Language Models (LLMs). The system utilizes the Retrieval-Augmented Generation (RAG) process to analyze and extract knowledge from external documents, enabling the creation of contextually accurate and relevant Q&As. At the core of this system is an Artificially Intelligent Agent (AI-Agent) designed to evaluate students’ knowledge through gamified interactions. The agent incorporates elements such as points, streaks, and narrative-driven scenarios to enhance engagement and motivation. The system was tailored for Spanish-speaking contexts and tested to assess the accuracy, quality, and difficulty of the generated questions, as well as its impact on students’ learning experience. By combining cutting-edge AI techniques with gamification techniques, this project aims to validate its effectiveness as an innovative and scalable educational tool, offering new opportunities for personalized learning and evaluation. |
| publishDate |
2025 |
| dc.date.accessioned.none.fl_str_mv |
2025-01-14T19:49:02Z |
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2025-12-31 |
| dc.date.issued.none.fl_str_mv |
2025-01-13 |
| dc.date.accepted.none.fl_str_mv |
2025-01-14 |
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Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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https://hdl.handle.net/1992/75407 |
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instname:Universidad de los Andes |
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reponame:Repositorio Institucional Séneca |
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eng |
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eng |
| dc.relation.references.none.fl_str_mv |
Hanyi Xu et al. Large Language Models for Education: A Survey. 2024. O. Pastushenko, T. Hrûska, and J. Zendulka. “Increasing students’ motivation by using virtual learning environments based on gamification mechanics: Implementation and evaluation of gamified assignments for students”. In: Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality. 2018, pp. 755–760. M. Jarnac de Freitas and M. Mira da Silva. “Systematic literature review about gamification in MOOCs”. In: vol. 38. 1. 2023, pp. 73–95. S. Deterding et al. “From game design elements to gamefulness: defining ”gamification””. In: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments. 2011. A. Domínguez et al. “Gamifying learning experiences: Practical implications and outcomes”. In: Computers & Education 63 (2013), pp. 380–392. T. B. Brown et al. “Language models are few-shot learners”. In: Advances in Neural Information Processing Systems. 2020. J. Wei et al. “Chain of Thought Prompting Elicits Reasoning in Large Language Models”. In: arXiv preprint arXiv:2201.11903 (2022). Ying-Hong Chan and Yao-Chung Fan. “A Recurrent BERT-based Model for Question Generation”. In: Proceedings of the 2nd Workshop on Machine Reading for Question Answering. Hong Kong, China: Association for Computational Linguistics, 2019, pp. 154–162. Yao Zhao et al. “Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks”. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics, 2018, pp. 3901–3910. Jiatao Gu et al. “Incorporating Copying Mechanism in Sequence-to-Sequence Learning”. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Berlin, Germany: Association for Computational Linguistics, 2016, pp. 1631–1640. Kishore Papineni et al. “Bleu: a method for automatic evaluation of machine translation”. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2002, pp. 311–318. Bernardo Leite and Henrique Lopes Cardoso. “Do Rules Still Rule? Comprehensive Evaluation of a Rule-Based Question Generation System”. In: Proceedings of the 15th International Conference on Computer Supported Education. 2023. Yicheng Sun, Hejia Chen, and Jie Wang. “Multiple-choice Question Generation for the Chinese Language”. In: Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. 2022. Y. Kido et al. “Automatic Question Generation for the Japanese National Nursing Examination Using Large Language Models”. In: Proceedings of the International Conference on Computer Supported Education. 2024. T. Chugh et al. “Intelligent Agents driven Data Analytics using Large Language Models”. In: 2023 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics (ICoABCD). 2023. M. Zhuge et al. “Language Agents as Optimizable Graphs”. In: arXiv.org (2024). LangGraph. LangGraph Documentation. https://langchain- ai.github.io/langgraph/. Accessed: 2024-11-15.35 Andreas Holzinger et al. “A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop”. In: Proceedings of the Arxiv Conference on Artificial Intelligence Research. 2017, pp. 1–12. E. Mosqueira-Rey, E. Hernández-Pereira, and D. Alonso-Ríos. “Human-in-the-loop Machine Learning: A State of the Art”. In: Proceedings of the Artificial Intelligence Review. 2023. Sigurd Schacht, Sudarshan Kamath Barkur, and Carsten Lanquillon. “Generative Agents to Support Students Learning Progress”. In: Libro de Actas. 2023. Cheng-Han Chiang and Hung-yi Lee. Can Large Language Models Be an Alternative to Human Evaluations? 2023. Aniket Deroy, Subhankar Maity, and Sudeshna Sarkar. MIRROR: A Novel Approach for the Automated Evaluation of Open-Ended Question Generation. 2024. S.E. Huber, K. Kiili, S. Nebel, et al. “Leveraging the Potential of Large Language Models in Education Through Playful and Game-Based Learning”. In: Educational Psychology Review. 2024. W. Du et al. “CareerSim: Gamification Design Leveraging LLMs For Career Development Reflection”. In: CHI Extended Abstracts. 2024. Y.-A. Bachiri, H. Mouncif, and B. Bouikhalene. “Artificial Intelligence Empowers Gamification: Optimizing Student Engagement and Learning Outcomes in Elearning and MOOCs”. In: International Journal of Engineering Pedagogy (iJEP) (2023). A. Hung. “A Critique and Defense of Gamification”. In: Proceedings of an unspecified conference or publication. 2017. S. Hallifax et al. “Adaptive Gamification in Education: A Literature Review of Current Trends and Developments”. In: European Conference on Technology Enhanced Learning. 2019. |
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46 páginas |
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Manrique Piramanrique, Rubén Franciscovirtual::22037-1Duarte Mantilla, Ernesto JoséKlopstock Triana, NicolásFacultad de Ingeniería::TICSw: Tecnologías de Información y Construcción de Software2025-01-14T19:49:02Z2025-12-312025-01-132025-01-14https://hdl.handle.net/1992/75407instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/This project focuses on the development of a personalized and gamified learning environment powered by the automatic generation of questions and answers (Q&A-AG) using Large Language Models (LLMs). The system utilizes the Retrieval-Augmented Generation (RAG) process to analyze and extract knowledge from external documents, enabling the creation of contextually accurate and relevant Q&As. At the core of this system is an Artificially Intelligent Agent (AI-Agent) designed to evaluate students’ knowledge through gamified interactions. The agent incorporates elements such as points, streaks, and narrative-driven scenarios to enhance engagement and motivation. The system was tailored for Spanish-speaking contexts and tested to assess the accuracy, quality, and difficulty of the generated questions, as well as its impact on students’ learning experience. By combining cutting-edge AI techniques with gamification techniques, this project aims to validate its effectiveness as an innovative and scalable educational tool, offering new opportunities for personalized learning and evaluation.Pregrado46 páginasapplication/pdfengUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería de Sistemas y ComputaciónAttribution-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfExploration of large language models in education: automated question and answer generation system and gamified learning environmentTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPLarge Language ModelsGamificationLearning EnvironmentRetrieval-Augmented GenerationIntelligent AgentMulti-Agent SystemQuestion & Answer Automatic GenerationGraph-orchestrated AgentsIngenieríaHanyi Xu et al. Large Language Models for Education: A Survey. 2024.O. Pastushenko, T. Hrûska, and J. Zendulka. “Increasing students’ motivation by using virtual learning environments based on gamification mechanics: Implementation and evaluation of gamified assignments for students”. In: Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality. 2018, pp. 755–760.M. Jarnac de Freitas and M. Mira da Silva. “Systematic literature review about gamification in MOOCs”. In: vol. 38. 1. 2023, pp. 73–95.S. Deterding et al. “From game design elements to gamefulness: defining ”gamification””. In: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments. 2011.A. Domínguez et al. “Gamifying learning experiences: Practical implications and outcomes”. In: Computers & Education 63 (2013), pp. 380–392.T. B. Brown et al. “Language models are few-shot learners”. In: Advances in Neural Information Processing Systems. 2020.J. Wei et al. “Chain of Thought Prompting Elicits Reasoning in Large Language Models”. In: arXiv preprint arXiv:2201.11903 (2022).Ying-Hong Chan and Yao-Chung Fan. “A Recurrent BERT-based Model for Question Generation”. In: Proceedings of the 2nd Workshop on Machine Reading for Question Answering. Hong Kong, China: Association for Computational Linguistics, 2019, pp. 154–162.Yao Zhao et al. “Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks”. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics, 2018, pp. 3901–3910.Jiatao Gu et al. “Incorporating Copying Mechanism in Sequence-to-Sequence Learning”. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Berlin, Germany: Association for Computational Linguistics, 2016, pp. 1631–1640.Kishore Papineni et al. “Bleu: a method for automatic evaluation of machine translation”. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2002, pp. 311–318.Bernardo Leite and Henrique Lopes Cardoso. “Do Rules Still Rule? Comprehensive Evaluation of a Rule-Based Question Generation System”. In: Proceedings of the 15th International Conference on Computer Supported Education. 2023.Yicheng Sun, Hejia Chen, and Jie Wang. “Multiple-choice Question Generation for the Chinese Language”. In: Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. 2022.Y. Kido et al. “Automatic Question Generation for the Japanese National Nursing Examination Using Large Language Models”. In: Proceedings of the International Conference on Computer Supported Education. 2024.T. Chugh et al. “Intelligent Agents driven Data Analytics using Large Language Models”. In: 2023 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics (ICoABCD). 2023.M. Zhuge et al. “Language Agents as Optimizable Graphs”. In: arXiv.org (2024).LangGraph. LangGraph Documentation. https://langchain- ai.github.io/langgraph/. Accessed: 2024-11-15.35Andreas Holzinger et al. “A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop”. In: Proceedings of the Arxiv Conference on Artificial Intelligence Research. 2017, pp. 1–12.E. Mosqueira-Rey, E. Hernández-Pereira, and D. Alonso-Ríos. “Human-in-the-loop Machine Learning: A State of the Art”. In: Proceedings of the Artificial Intelligence Review. 2023.Sigurd Schacht, Sudarshan Kamath Barkur, and Carsten Lanquillon. “Generative Agents to Support Students Learning Progress”. In: Libro de Actas. 2023.Cheng-Han Chiang and Hung-yi Lee. Can Large Language Models Be an Alternative to Human Evaluations? 2023.Aniket Deroy, Subhankar Maity, and Sudeshna Sarkar. MIRROR: A Novel Approach for the Automated Evaluation of Open-Ended Question Generation. 2024.S.E. Huber, K. Kiili, S. Nebel, et al. “Leveraging the Potential of Large Language Models in Education Through Playful and Game-Based Learning”. In: Educational Psychology Review. 2024.W. Du et al. “CareerSim: Gamification Design Leveraging LLMs For Career Development Reflection”. In: CHI Extended Abstracts. 2024.Y.-A. Bachiri, H. Mouncif, and B. Bouikhalene. “Artificial Intelligence Empowers Gamification: Optimizing Student Engagement and Learning Outcomes in Elearning and MOOCs”. In: International Journal of Engineering Pedagogy (iJEP) (2023).A. Hung. “A Critique and Defense of Gamification”. In: Proceedings of an unspecified conference or publication. 2017.S. Hallifax et al. “Adaptive Gamification in Education: A Literature Review of Current Trends and Developments”. 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