Assessment of Learner Engagement and Expert Evaluations of AI-Generated Versus Human-Created Interactive Content in an Online Course
Bibliographic record
Abstract
Generative artificial intelligence (GenAI) has introduced a novel aspect to educational methodologies and sparked fresh dialogues regarding the creation and evaluation of instructional resources. This project seeks to investigate the impact of GenAI on the development and assessment of online course materials and learners’ engagement with these materials in the online learning environment. The study analyzed GenAI-generated multiple-choice questions, fill-in-the-blank exercises, and true-false activities during 3 weeks of a 14-week online course. Subject matter experts assessed these documents in regards to content, relevance, and clarity. Data was collected through an online form with open-ended questions. The interactions of learners with the GenAI-created learning activities were analyzed using log records of the learning management system and compared to the content provided by the course instructor regarding interaction levels. The study’s conclusions elucidate the capability of GenAI technologies to produce course-specific content and their efficacy in education. We stress that human specialists’ critical evaluations play a crucial part in improving the pedagogical validity of GenAI-powered learning materials. Further research into topics including the ethical dimension, the effect on academic achievement, and student motivation is recommended.
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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 | 0.006 | 0.030 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".