LLM-Generated Personalized Analogies to Foster AI Literacy in Adult Novices
Bibliographic record
Abstract
Broad Al literacy is essential in today's rapidly advancing technological landscape, extending beyond Al specialists to encompass the general public. However, the complexity of Al concepts poses significant barriers to learning for individuals without prior Al knowledge. While teaching through analogies is a well-recognized method to simplify complex information by connecting it to familiar concepts, adapting these analogies to match individual learner profiles remains a substantial challenge. This paper addresses this gap by proposing a novel method for personalizing educational analogies, enhancing the accessibility and engagement of AI concepts for a diverse audience. Our approach uses Large language models (LLMs) to dynamically tailor content to each learner's cognitive and cultural contexts, grounded in educational theories and practices. Utilizing a crowdsourced AIB testing framework through Prolific (N-60), this research contrasts conventional instructional methods with content incorporating LLM-enhanced personalized analogies. Data collection comprised pre- and post-tests, activity logs, and surveys featuring Likert-scale and open-ended questions. Quantitative analysis of key learning outcomes revealed significant improvements in comprehension and retention, evidenced by enhanced pre-and post- test scores (p < 0.01 and p < 0,05, respectively) and motivation, as indicated by increased engagement in survey responses (p < 0.05). Qualitative analysis revealed a need for more examples and visual aids to complement analogies and a preference for balancing analogies with detailed technical content. This study demonstrates the potential of Al-generated analogies to make complex Al concepts more accessible and engaging. Future research should refine analogy generation. incorporate multimedia elements, and explore long-term and cross-cultural impacts to further enhance Al education.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".