Summary of Personalizing Explanations Of Ai-driven Hints to Users’ Cognitive Abilities: An Empirical Evaluation, by Vedant Bahel et al.
Personalizing explanations of AI-driven hints to users’ cognitive abilities: an empirical evaluation
by Vedant Bahel, Harshinee Sriram, Cristina Conati
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The researchers investigate how to tailor the explanations provided by an Intelligent Tutoring System (ITS) to improve student engagement and learning outcomes. They focus on students with low levels of Need for Cognition and Conscientiousness, who typically don’t engage with explanations but would benefit from them if personalized. The proposed approach involves generating hints that cater to these students’ individual needs. The authors conducted a user study to evaluate the effectiveness of their method, finding significant increases in interaction with hint explanations, understanding of hints, and learning outcomes among target users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are trying to make an educational computer program better at explaining things to students. They want to help students who don’t naturally understand why they should learn certain things. To do this, the program will give personalized explanations that cater to each student’s unique needs. This means the program will adapt its explanation style and language to match the individual student’s learning style and personality. The researchers tested their approach with a group of students and found it was effective in increasing student engagement and understanding. |
