Transformative Pedagogy: Leveraging Generative AI Tools for Enhanced Learning Experiences
Bibliographic record
Abstract
This study employs generative AI to revamp a core engineering course, thermodynamics, to boost student engagement and comprehension. In collaboration with faculty, students, and AI specialists, the effort explores effective AI tools and strategies for question generation and the creation of digital aids, promising a significant impact on student participation and overall learning experience. This approach not only enhances learning experiences but also fosters a culture of innovation, suggesting significant potential for applying these methods across various courses. The initiative aims to refine pedagogical practices through strategic AI tool integration, highlighting the evolving role of technology in education. The study integrates AI models for educational content, employing advanced AI like GPT-3.5, 4, Gemini, DALL-E, Eduaide, and Llama 2. It fine-tunes AI settings for optimal performance and rigorously assesses the quality of generated content and images, revealing AI's potential in creating relevant educational materials. However, challenges in accurate visual representation persist.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".