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Generative AI for Customizable Learning Experiences

2024-04-05Artigo científico Acesso aberto
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dc.contributor.authorPesovski, Ivica
dc.contributor.authorSantos, Ricardo Miguel
dc.contributor.authorHenriques, Roberto
dc.contributor.authorTrajkovik, Vladimir
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblMolecular Diversity Preservation International (MDPI)
dc.date.accessioned2024-04-09T01:07:28Z
dc.date.available2024-04-09T01:07:28Z
dc.date.issued2024-04-05
dc.descriptionPesovski, I., Santos, R. M., Henriques, R., & Trajkovik, V. (2024). Generative AI for Customizable Learning Experiences. Sustainability, 16(7), 1-23. Article 3034. https://doi.org/10.3390/su16073034
dc.description.abstractThe introduction of accessible generative artificial intelligence opens promising opportunities for the implementation of personalized learning methods in any educational environment. Personalized learning has been conceptualized for a long time, but it has only recently become realistic and truly achievable. In this paper, we propose an affordable and sustainable approach toward personalizing learning materials as part of the complete educational process. We have created a tool within a pre-existing learning management system at a software engineering college that automatically generates learning materials based on the learning outcomes provided by the professor for a particular class. The learning materials were composed in three distinct styles, the initial one being the traditional professor style and the other two variations adopting a pop-culture influence, namely Batman and Wednesday Addams. Each lesson, besides being delivered in three different formats, contained automatically generated multiple-choice questions that students could use to check their progress. This paper contains complete instructions for developing such a tool with the help of large language models using OpenAI’s API and an analysis of the preliminary experiment of its usage performed with the help of 20 college students studying software engineering at a European university. Participation in the study was optional and on voluntary basis. Each student’s tool usage was quantified, and two questionnaires were conducted: one immediately after subject completion and another 6 months later to assess both immediate and long-term effects, perceptions, and preferences. The results indicate that students found the multiple variants of the learning materials really engaging. While predominantly utilizing the traditional variant of the learning materials, they found this approach inspiring, would recommend it to other students, and would like to see it more in classes. The most popular feature were the automatically generated quiz-style tests that they used to assess their understanding. Preliminary evidence suggests that the use of various versions of learning materials leads to an increase in students’ study time, especially for students who have not mastered the topic otherwise. The study’s small sample size of 20 students restricts its ability to generalize its findings, but its results provide useful early insights and lay the groundwork for future research on AI-supported educational strategies.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent23
dc.format.extent1492311
dc.identifier.doi10.3390/su16073034
dc.identifier.issn2071-1050
dc.identifier.otherPURE: 87864350
dc.identifier.otherPURE UUID: 47f6ab89-7fa2-4500-812e-39048b750950
dc.identifier.othercrossref: 10.3390/su16073034
dc.identifier.otherScopus: 85190279516
dc.identifier.otherWOS: 001201029300001
dc.identifier.otherORCID: /0000-0002-4862-8177/work/157247837
dc.identifier.urihttp://hdl.handle.net/10362/165975
dc.identifier.urlhttps://www.scopus.com/pages/publications/85190279516
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001201029300001
dc.language.isoeng
dc.peerreviewedyes
dc.subjectpersonalized learning
dc.subjectAI for learning
dc.subjectcharacter-based learning
dc.subjectautomated content generation
dc.subjectLLMs in education
dc.subjectinnovative teaching methods
dc.subjectlearning management systems
dc.subjectComputer Science (miscellaneous)
dc.subjectGeography, Planning and Development
dc.subjectRenewable Energy, Sustainability and the Environment
dc.subjectEnvironmental Science (miscellaneous)
dc.subjectEnergy Engineering and Power Technology
dc.subjectHardware and Architecture
dc.subjectComputer Networks and Communications
dc.subjectManagement, Monitoring, Policy and Law
dc.subjectSDG 4 - Quality Education
dc.titleGenerative AI for Customizable Learning Experiencesen
dc.typejournal article
degois.publication.firstPage1
degois.publication.issue7
degois.publication.lastPage23
degois.publication.titleSustainability
degois.publication.volume16
dspace.entity.typePublication
rcaap.rightsopenAccess

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