Enhancing Research Efficiency: Integrating GenAI in Academic Research with Institutional Approved or Licensed Tools
Bibliographic record
Abstract
The rapid emergence of generative AI (GenAI) has greatly impacted the academic research landscape, offering both challenges and opportunities. A course director at the Faculty of Dentistry University of Toronto piloted an AI-focused initiative. Students were asked to submit two versions of an essay on a research topic: one written by themselves and another generated by AI simply using Microsoft copilot with only minor directions. A librarian was invited to the in-class seminar discussions to gather insights into students’ AI experiences and perceived challenges. Based on these findings, recognizing a growing need for students to understand and effectively integrate GenAI tools into their research process, we designed a workshop to promote additional institutional licensed tools such as Scopus AI, Web of Science Research Assistant among others. In addition, we show students some practical strategies for leveraging AI tools throughout various research stages. By promoting those tools, our objective is to empower our students to develop critical evaluation skills, use those AI tools more efficiently and improve productivity. Ultimately, it will lead to improved research outcomes. This will also guide course directors in their future implementation of AI components in their respective programs. The workshop was scheduled for early 2025. A post-workshop survey will assess the effectiveness of this approach and guide future AI-related library instructional sessions. This poster presentation will share some key findings from the classroom assignment, the development and content of the workshop, and the anticipated results. Through this case study, we aim to contribute actionable insights for academic libraries navigating the continuous and changing landscape of AI and research.
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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 | 0.091 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".