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Seeing the Forest from the Trees: Unveiling the Landscape of Generative AI for Education Through Six Evaluation Dimensions
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID.ORCID iD: 0000-0002-0456-6664
Stockholm University, Digital Futures, Stockholm, Sweden.
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Digital futures.ORCID iD: 0000-0002-8543-3774
2024 (English)In: Technology Enhanced Learning for Inclusive and Equitable Quality Education - 19th European Conference on Technology Enhanced Learning, EC-TEL 2024, Proceedings, Springer Nature , 2024, p. 99-105Conference paper, Published paper (Refereed)
Abstract [en]

Artificial intelligence (AI) holds significant promise as a technology that may improve the quality of educational practices. This includes specialized AI-powered technologies tailored for education and general AI-based technologies, including recently popular generative AI tools that stakeholders are increasingly adapting for teaching and learning. Integrating AI tools into educational settings holds numerous potential pedagogical benefits, such as assisting teachers in planning lessons, promoting personalization, and enhancing student autonomy. However, concerns about bias and discrimination linked to the use of these technologies have rapidly emerged. Today, standardized evaluation criteria to assess the potential contribution of such tools to education and their reliability within the learning and teaching context are lacking. To address this gap, we build on an existing taxonomy for the evaluation of open educational resources (OER) to better suit the unique features of generative AI. The result is a six-dimensional evaluation approach that includes descriptive, pedagogical, representational, communication, scientific content, as well as the ethical and transparency dimension. We then apply this approach to examine the educational potential and ethical concerns around 30 AI tools. The analysis facilitates a critical mapping of the potential and risks of AI-powered technologies in education settings.

Place, publisher, year, edition, pages
Springer Nature , 2024. p. 99-105
Keywords [en]
Algorithm Bias, Generative AI, Open Educational Resource (OER)
National Category
Pedagogy
Identifiers
URN: urn:nbn:se:kth:diva-354664DOI: 10.1007/978-3-031-72312-4_12ISI: 001332998900012Scopus ID: 2-s2.0-85205311849OAI: oai:DiVA.org:kth-354664DiVA, id: diva2:1904560
Conference
19th European Conference on Technology Enhanced Learning, EC-TEL 2024, Krems, Austria, Sep 16 2024 - Sep 20 2024
Note

Part of ISBN 9783031723117

QC 20250922

Available from: 2024-10-09 Created: 2024-10-09 Last updated: 2025-09-22Bibliographically approved

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Feldman Maggor, YaelViberg, Olga

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