Adaptic Didactic Infrastructure for Novice Learners with Pedagogy Using AI
Bibliographic record
Abstract
Artificial Intelligence (AI) has emerged as a transformative force in education, offering adaptive and personalized learning experiences that extend beyond conventional instructional models. Traditional pedagogical methods often lack interactivity, adaptability, and inclusivity, limiting holistic development among novice learners. This paper proposes an Adaptive Didactic Infrastructure (ADI) powered by AI that integrates academic learning with life skill development through interactive games, adaptive assessments, and multimodal content delivery. Unlike existing tutoring systems, the ADI emphasizes deeper conceptual comprehension, critical thinking, and creativity by leveraging natural language understanding and AI-driven visualization tools. The framework ensures equitable learning opportunities through personalized progress tracking, real-time feedback, and dynamic adjustment of instructional strategies. The system not only enhances academic outcomes but also fosters curiosity, analytical reasoning, and problem-solving abilities among young learners. This study highlights the methodological design, experimental results, and potential applications of the ADI framework as a scalable, inclusive, and future-ready educational paradigm.
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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 | 0.003 |
| 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".