Students Experience on Self-Study through AI
Bibliographic record
Abstract
This study aimed to explore students' experiences with AI-assisted self-study, focusing on their engagement with AI tools, learning outcomes, perceived challenges and limitations, available support and resources, and overall perceptions of AI in education. Employing a qualitative research design, this study conducted semi-structured interviews with 20 students who have used AI tools for self-study. Participants were selected through purposive sampling to ensure a diverse representation across different academic disciplines, levels of study, and demographics. Thematic analysis was used to identify patterns and insights within the interview data. Five major themes were identified: Engagement with AI Tools, Learning Outcomes, Challenges and Limitations, Support and Resources, and Perceptions of AI in Education. Students reported positive impacts of AI on engagement and learning outcomes, including enhanced knowledge retention and skill development. However, technical issues, content limitations, and concerns about data privacy were highlighted as significant challenges. Support from AI in terms of tutoring and guidance was deemed beneficial, while perceptions of AI in education ranged from optimism about future possibilities to concerns about ethical implications. AI tools can significantly enhance self-study by providing personalized and interactive learning experiences that cater to individual student needs. While the potential benefits are substantial, addressing technical, ethical, and accessibility challenges is crucial for maximizing the positive impacts of AI in education. This study underscores the importance of ongoing dialogue and collaboration among educators, students, and technology developers to align AI tools with educational goals and ethical standards.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".