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Atherton, P, Topham, L and Khan, W (2024) AI and student feedback. In: EDULEARN24 Proceedings . (Edulearn 2024 - 16th International Conference on Education and New Learning Technologies, 1st Jul -3rd Jul 2024, Palma, Spain).
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Abstract
AI has the potential to have a transformative effect on teaching, learning, and assessment. This paper reviews recent literature on AI Education (AIEd). The paper makes recommendations for the development of edtech learning platforms using AI. This paper reviews recent literature on AI Education (AIEd). The review was conducted in three stages: the first and second were systematic reviews conducted in 2023; the third stage provides a narrative review of emerging issues since Chat GPT has been part of debates about AIEd. The literature reflects positive progress regarding personalised learning journeys, AI-enhanced grading and evaluation, conversational agents for speaking and listening practice, and early interventions for struggling students. The review extends to the incorporation of AI within educational administration, encompassing learning analytics and predictive capabilities. The originality of this study arises from the paucity of studies on AI in the context of bricks and mortar school classrooms. The rigour of the study is the result of the systematic selection of current, targeted peerreviewed studies. Emerging studies each make their own contributions to knowledge in areas such as learning design, adaptive learning, modelling, and knowledge mapping. There were concerns about privacy, biased input leading to stereotypical judgments, and ethics and privacy. Furthermore, the study acknowledges the need for ongoing research into AI Ed.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | L Education > LB Theory and practice of education Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | Computer Science and Mathematics Education |
| Publisher: | INTED |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 17 Jul 2024 12:06 |
| Last Modified: | 24 Oct 2024 12:15 |
| DOI or ID number: | 10.21125/edulearn.2024 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/23757 |
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