Bibliographic record
Abstract
In postsecondary education institutions, where innovative technologies continually reshape research and pedagogical approaches, the integration of generative artificial intelligence (GenAI) tools presents promising avenues for enhancing student learning experiences. This study assesses the efficacy of integrating GenAI tools, specifically chatbots, to ameliorate learning outcomes and mitigate test anxiety among postsecondary students. Within the context of an introductory psychology course, 40 participants engaged with chatbots as supplementary “study confidantes” for exam preparation and as facilitators for an essay grading task. The results from surveys administered to the participants indicated that most students found the chatbots helpful in aiding comprehension of course materials. Moreover, a substantial proportion of respondents reported an enhanced understanding of how to structure an academic paper because of their essay grading activity. Despite the favourable perception of chatbot assistance with learning course material and fostering insights into structuring scholarly essays, no discernible alterations in the levels of test anxiety among students were observed. Overall, this research underscores the latent potential of chatbots as pedagogical adjuncts, furnishing instructive insights for educators aiming to innovate instructional methodologies and optimize student learning paradigms within the domain of postsecondary education. Keywords: GenAI tools, assessments, learning outcomes
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".