In an AI-driven era, students must learn to use AI tools responsibly—enhancing, not replacing, original thought. This means grounding all work in logic, clarity, and evidence-based reasoning, even when AI cannot reliably generate citations.

Table of Contents

• Teaching Philosophy
• Teaching Practice
• Impact
• Supplemental Material
  

Teaching Philosophy

I believe graduate science education should mirror the real-world environments students will encounter in academia or industry. My teaching emphasizes authentic, immersive learning experiences that develop critical thinking, clear communication, and ethical engagement with emerging technologies. In an AI-driven era, students must learn to use AI tools responsibly—enhancing, not replacing, original thought. This means grounding all work in logic, clarity, and evidence-based reasoning, even when AI cannot reliably generate citations. Through structured journal clubs modeled after peer review, NSF-style proposal development, and bioethical debates in modern biotechnology, I challenge students to analyze, synthesize, and create with intellectual integrity. By guiding them through iterative, collaborative processes that reflect professional scientific workflows, I prepare them to excel as both subject-matter experts and adaptable, ethical contributors in a rapidly evolving scientific landscape.

Training Graduate Students in Real-World Scientific Practice with Responsible AI Integration

Teaching Practice

In MBBE/MICR 602: Molecular Biology and Genetics, I implement an integrated teaching practice designed to simulate the real-world scientific research environment (Supplemental Material 1, syllabus). The course weaves together three innovative components—journal club peer-review simulation, NSF-style research proposal development, and bioethical debate in modern biotechnology—into a cohesive framework supported by structured assignments, peer engagement, instructor feedback, and clear guidelines for responsible AI use.

In most graduate bioscience programs, these skills—critical reading, grant writing, and ethical reasoning—are taught separately. In this course, they are intentionally integrated, giving students an immersive, end-to-end experience that mirrors the complex demands of modern research careers.

1. Journal Club as Peer-Review Simulation
Students choose recent preprint articles on topics such as gene editing, epigenetics, or synthetic biology. Each student (acting as “reviewer-in-chief”) delivers a 5-minute in-class pitch summarizing the paper’s experimental design and findings, aiming to recruit classmates as peer reviewers.

Review teams then:
1) Discuss and evaluate the study’s strengths and weaknesses
2) Write a formal peer review using a structured rubric (Supplemental Material 2)
3) Present their review in class, share it with the corresponding author, and post it to bioRxiv (Supplemental Material 3)

This activity replaces AI-generated summaries with firsthand critical engagement with the literature. Students learn to identify methodological gaps, interpret data rigorously, and write in the professional tone expected in journal peer review.

2. NSF-Style Research Proposal with Ethical AI Integration
Mid-semester, students prepare a 1–2-page research pre-proposal on a self-chosen course-related topic (Supplemental Material 4). The proposal includes background, objectives, hypotheses, methodology, and broader impacts.

The process includes:
1) Peer review using NSF-style evaluation criteria (Supplemental Material 5)
2) Final submission revised in response to both peer and instructor feedback (Supplemental Material 6)
3) A “Response to Feedback” section simulating the grant resubmission process (Supplemental Material 7)

AI tools are encouraged to use for brainstorming, language refinement, and grammar checks—but not for generating scientific reasoning or fabricating citations (Supplemental Material 8). To reinforce citation integrity, proposals are capped at seven references, each verified for accuracy by a peer reviewer (Supplemental Material 9). This structured process develops research design, professional writing, and critical evaluation skills, while modeling responsible AI integration in academic work.

3. Ethics in Bioscience: Debate, Reflection, and Student-Led Publication
Students engage with pressing ethical questions, such as:
1) Should legislation permit the creation of CRISPR babies?
2) Are GMOs ethically acceptable in modern agriculture?

Assignments include an evidence-based position paper and participation in a moderated class debate (Supplemental Material 10). Debates consistently generate thoughtful, well-supported arguments (Supplemental Material 11), encouraging respectful discourse across differing viewpoints.

The impact of this component has extended beyond the classroom—Spring 2025 debates inspired several students to co-author a review manuscript on GMO ethics (Supplemental Material 12), an unassigned, student-led scholarly output that is now being prepared for peer-reviewed publication.

Alignment with Student Needs & Accessibility
1) Choice-based topics increase motivation and personal investment
2) Scaffolded feedback supports learners across skill levels
3) Multiple formats (presentations, writing, review) accommodate varied learning preferences

Results & Replicability
The integrated approach has produced:
1) Published-quality peer reviews shared with authors and posted publicly (Supplemental Material 3)
2) Policy- and regionally-relevant proposals (Supplemental Material 7)
3) Student-led manuscripts emerging from classroom discourse (Supplemental Material 12)
4) Shifts in student perspectives on controversial biotechnology topics (see impact section)

The model is replicable in other graduate courses by combining:
1) Peer-review templates modeled on journal practice
2) Structured proposal cycles with resubmission steps
3) Ethics prompts paired with position-paper writing
4) AI-use policies with built-in verification

Implementation Considerations
1)Timely, detailed feedback is essential; peer review helps manage workload while deepening learning
2) Clear AI-use training early in the semester prevents misuse and sets consistent expectations
3) Well-designed templates and rubrics streamline assessment and maintain quality across outputs

Conclusion
This teaching practice offers a comprehensive, real-world training environment for graduate bioscience students. By uniting peer review, grant proposal development, and bioethical debate—and embedding responsible AI literacy—the course cultivates critical thinking, scholarly communication, and ethical awareness. Students leave not only with technical expertise, but also with the professional skills and moral grounding required to thrive in a scientific landscape increasingly shaped by AI, public accountability, and interdisciplinary collaboration.

Impact

This integrated teaching model has produced measurable, authentic outcomes in graduate student learning. Students generate high-quality peer reviews (Supplemental Material 3) and research proposals (Supplemental Material 7) at a standard suitable for real-world submission. Reviews have been shared directly with preprint authors and posted publicly on bioRxiv, while one student proposal was submitted to a competitive graduate scholar program.

For many, this is the first experience with a true iterative writing process that mirrors professional grant development. The combination of feedback cycles, public-facing scholarship, and ethical reasoning builds skills in research design, academic writing, and scientific communication that endure beyond the course.

A particularly striking outcome occurred in Spring 2025, when class debates on GMO ethics led students to initiate their own co-authored review manuscript (Supplemental Material 12)—an unassigned, collaborative publication effort that exemplifies intellectual ownership.
Survey results reinforce the transformative effect: 50–60% of students reported a shift in their stance on topics like CRISPR and GMOs after the structured debate and position-paper sequence (Supplemental Materials 13–15).

In a research era increasingly influenced by AI, this practice equips students with the judgment, rigor, and collaborative skills essential for both academic and professional success.

Supplemental Material

• Supplemental Materials