AI's Promise and Peril: Evaluating the SHAPE Framework on Academic Commitment and Gender Outcomes in Higher Education
Bibliographic record
Abstract
This study investigates the implementation of the SHAPE framework in undergraduate business courses at Tecnologico de Monterrey, assessing its impact on enhancing learning, preference for AI use, academic commitment, and development of reflection and research skills with Artificial Intelligence (AI) tools.A mixed-method approach was employed, replicating and expanding upon a previous study using a larger and more diverse sample involving 90 students in the fifth semester of International Business across two campuses.Data collection included quantitative surveys, student reflections, and course deliverable analysis.Results confirmed the overall positive perception of AI's benefits but revealed significant gender disparities.Women exhibited higher acceptance and engagement with AI, especially concerning reflective and research skills, while men favored AI for practical problem-solving.The findings of this study are consistent with existing literature on gender and technology adoption and underscore the necessity of inclusive pedagogical strategies that leverage AI's potential while accommodating diverse learning styles and preferences.The research highlights the importance of considering gender-specific needs when designing and implementing AI-integrated educational technologies.Limitations include a restricted sample to one-degree program across two campuses and the cross-sectional design prevents an evaluation of the long-term effects.Future research should examine the model's long-term impact, comparative effectiveness, and broader applicability across diverse educational contexts.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".