Personalisation is not Guaranteed: The Challenges of Using Generative AI for Personalised Learning
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 14785 LNCS, Page: 40-49
2024
- 30Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures30
- Readers30
- 30
Conference Paper Description
Personalised learning promises to free learners from the shackles of traditional one-size-fits-all classrooms and fulfil the potential of every child. Generative artificial intelligence (GenAI) is seen as the solution that will bring personalised learning to all learners. Here, we argue that GenAI may not only fall short of this expectation but also hinder the development of the competencies necessary to unlock learners’ potential. Our first argument posits that although personalised learning aims to diversify instruction to meet the needs of individual learners, the indiscriminate use of GenAI risks homogenising learners’ performance and creativity, limiting the discovery of their potential. We argue that effective use of GenAI requires curiosity, critical thinking, self-regulation and metacognition. Our second argument is that these skills are not enhanced by GenAI but rather might suffer from its unregulated use. Thus, while GenAI has considerable potential to enhance learning, learners will not reap these benefits by default. Instead, to make the most of the opportunities GenAI offers, the education system must invest more in developing human skills rather than technology.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85200649783&origin=inward; http://dx.doi.org/10.1007/978-3-031-65881-5_5; https://link.springer.com/10.1007/978-3-031-65881-5_5; https://dx.doi.org/10.1007/978-3-031-65881-5_5; https://link.springer.com/chapter/10.1007/978-3-031-65881-5_5
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know