AI_for_biomedical_students

Practical Strategies: AI Pedagogy (Practice) & HEA Senior Fellowship Guidance

Higher Education Academy — Senior Fellowship: ideas for an AI × pedagogy portfolio

(A) quick framing tied to the UK Professional Standards Framework (so you can map evidence to SFHEA), (B) teaching/design ideas you can run as modules/activities, (C) assessment ideas (with rubrics and anti-cheating/process suggestions), and (D) the kinds of artefacts and narrative structure that make a strong Senior Fellowship submission. Demonstrate strategic leadership, scholarship, and mentoring (the heart of Senior Fellowship).

A. Quick framing — map to UKPSF/Senior-Fellowship priorities

When writing your evidence, explicitly link activities to UKPSF dimensions:

For Senior Fellowship emphasise: strategic influence beyond a single module (mentoring colleagues, leading policy or curriculum change), evidence of sustained impact, and demonstrable leadership in developing others.

B. AI × Pedagogy module / teaching ideas (with learning outcomes)

  1. Foundations: “AI Literacy for Academics & Students” (short course/workshop)

    • Outcomes: evaluate LLM outputs; design safe prompts; critique ethical and epistemic limits.
    • Activities: live LLM demos, prompt labs, source-tracing workshop, mini research critiques.
    • Evidence: workshop materials, attendance numbers, pre/post confidence survey.

  2. “Designing Learning with AI” (staff CPD series)

    • Outcomes: integrate AI tools into assessment design responsibly; create inclusive AI-guidelines for a programme.
    • Activities: co-design clinics, peer-review of assessment briefs, pilot projects.
    • Evidence: programme-level policy drafts, testimonial from colleagues, rollout statistics.

  3. Applied module: “AI in Practice” (project-based, discipline-specific)

    • Outcomes: build and critique an AI-enabled solution for a domain problem; reflect on social impacts.
    • Activities: team projects, stakeholder interviews, mini user testing.
    • Evidence: student projects, external partner feedback, rubric-marked artefacts.

  4. Assessment-focused: “Assessing in the Age of AI” (seminar & toolkit)

    • Outcomes: create robust, authentic assessments that cannot be outsourced to LLMs; use AI for scalable formative feedback.
    • Activities: red-team assessments for vulnerability to cheating, design of staged submissions.
    • Evidence: redesign briefs, before/after cheating incidence data, student performance.

  5. Critical AI seminar series (ethics, historiography, policy)

    • Outcomes: situate AI development historically, evaluate ethical frameworks, influence departmental policy.
    • Activities: invited talks, student-led debates, policy-writing labs.
    • Evidence: seminar programme, minutes showing policy change, reflective essays.

C. Concrete assessment ideas (with rubrics & anti-cheating measures)

1) Annotated AI-assisted submission (individual, formative)

2) Authentic project (group, summative)

3) Source-verification & fact-check challenge (individual)

4) Oral defense / viva for key submissions

5) Peer review + calibrated rubric

6) AI-design ethical impact statement (mini-essay)

Sample rubric (for an AI-enabled group project, 100 points)

Provide exemplary descriptors for each band (high/medium/low) in the rubric.

D. Anti-contract-cheating & academic integrity design features

E. Evidence & artefacts to collect for Senior Fellowship

Make a deliberate evidence plan — collect these:

F. Narrative structure for sections of your SFHEA submission (practical template)

For each claim of influence/impact use this micro-structure (keep linking A/K/V):

  1. Claim — single sentence of what you did and which UKPSF dimensions it addresses.
  2. Context & scale — where, when, how many students/colleagues/programmes were affected.
  3. Activity — concrete actions you led (design, workshops, policy).
  4. Evidence & outputs — list artefacts (attach or reference).
  5. Outcomes & impact — measurable change (student performance, curriculum adoption, staff uptake).
  6. Reflection — what you learned, what you would change, how you will scale/transfer.

Repeat across 3–5 major claims showing sustained leadership and influence.

G. Quick examples you can drop into evidence statements

(Do not quote percentages unless you have the data—replace X with real figures or descriptive terms.)

H. A few low-effort, high-impact starter moves

Quick Practical Strategies for Teaching AI in Biomedicine (Practice)

How this maps to HEA Professional Standards (useful for Fellowship evidence)

Applying for HEA Senior Fellowship (UK) — Practical Steps

  1. Identify the route and institutional lead: contact your institution’s recognised HEA Fellowship coordinator or staff-development team to confirm the Senior Fellowship (Descriptor 3) pathway and institutional requirements.
  2. Gather a mentor/peer reviewer: secure a colleague or mentor experienced with HEA applications to advise and review drafts.
  3. Map activities to UKPSF explicitly: prepare a simple table that maps each claim to specific UKPSF dimensions (Area of Activity, Core Knowledge, Professional Values) and links to evidence.
  4. Collect evidence artifacts (examples):
    • Module/program design documents and learning outcomes showing curriculum leadership.
    • Sample teaching materials (notebooks, slide packs) and deployment artefacts (Docker/Binder links).
    • Records of student feedback and evaluation (summative and formative), with reflective commentary on how you acted on feedback.
    • Evidence of mentoring, supervision or leadership (mentoring records, workshop leadership, curriculum coordination).
    • Assessment design examples showing fairness, validity, and alignment to learning outcomes.
    • Outputs demonstrating impact (colleague uptake, changes to assessment, improved student outcomes).
  5. Draft the reflective narrative (supporting statement):
    • Structure: context → activity (what you did) → evidence (what shows it happened) → impact (who benefited and how) → reflection and development.
    • Explicitly cross-reference evidence to PSF claims (e.g., “A2, K2, V1 — see evidence 3: module handbook”).
  6. Obtain internal review and revise: circulate to mentor, peers, and your institution’s panel for feedback; respond and refine.
  7. Submit via your institution: follow local submission and moderation processes; include external reviewers if required by your institution.

Suggested Evidence Checklist (compact)

Practical Tips for a Strong Senior Fellowship Claim

Teaching Strategy: AI and Pedagogy for Biomedicine

Summary

A focused, practical strategy for teaching AI to biomedical students that emphasizes clinical relevance, interpretability, reproducibility, and ethics. Blend conceptual teaching, hands-on data labs, and authentic assessments to prepare students for real-world biomedical AI tasks.

Learning Objectives

Course Structure (high-level)

Teaching Methods

Sample Week-by-Week (8-week short course)

Week 1 — Intro & Foundations: probability, bias, overview of biomedical AI successes/failures. Week 2 — Data Wrangling: missing data, normalization, labeling challenges in clinical datasets. Week 3 — Supervised Learning: linear models, decision trees; biomedical performance metrics (sensitivity/specificity, ROC/PR). Week 4 — Model Interpretation: SHAP/LIME, saliency for images, case explanations for clinicians. Week 5 — Imaging & Signals: basics of convolutional ideas and time-series handling; pitfalls in imaging datasets. Week 6 — NLP in Biomedicine: clinical notes, entity extraction, de-identification concerns. Week 7 — Reproducibility & Deployment: containers, notebooks, CI for data pipelines, documentation (model cards). Week 8 — Ethics & Final Presentations: harm analysis, bias audit, student project presentations.

Assessment Strategies

Example Assignment Prompt (brief)

Task: Using the provided de-identified dataset of patient vitals, build a model to predict 48-hour deterioration risk. Deliverables:

Grading rubric highlights: technical correctness (30%), reproducibility (25%), interpretability & clinical framing (25%), ethics & limitations (20%).

Tools & Resources

Ethics, Privacy & Safety

Implementation Tips for Instructors

Measuring Success & Iteration

Short Reading & Resource List

Appendix: example rubric, sample notebook structure, and links to templates can be provided on request.

Data visualization and scavenger hunts: John Snow and the COVID-19 pandemic

This is a compelling narrative for a Senior Fellowship (SFHEA) application. It demonstrates leadership in digital innovation, the design of inclusive learning environments, and a scholarly approach to mapping historical theory onto modern global challenges.

Below is a draft “Short Paper” and a curated reflection designed to align with the Professional Standards Framework (PSF 2023).

🗺️ Spatial Intelligence in the Age of AI: From Broad Street to Big Data

Author: [Your Name]

Subject: Data Visualization & Pedagogical Innovation

The Persistent Power of “Where”

In 1854, Dr. John Snow’s map of the Soho cholera outbreak did more than find a broken pump; it invented a new way of seeing. By translating abstract death tolls into spatial points, Snow moved public health from superstition (miasma) to spatial evidence.

In today’s “Poly-crisis” world—defined by climate change, pandemics, and urban inequality—spatial visualization remains our most potent tool for sense-making. Whether it is tracking the spread of a respiratory virus in Wuhan or mapping “food deserts” in modern London, the ability to overlay disparate datasets onto a physical landscape allows us to identify systemic failures that statistics alone would hide.

The Role of AI in Modern Pedagogy

The “Syntax Wall” is the greatest barrier for undergraduates learning data science. Students often get bogged down in Python errors before they ever reach the “Aha!” moment of discovery. AI tools (LLMs) act as a pedagogical catalyst in three distinct ways:

  1. Lowering the Barrier to Entry: AI can generate boilerplate code (as seen in our Folium exercise), allowing students to focus on interpretation and ethics rather than debugging indentation.
  2. Synthetic Scaffolding: AI allows educators to create “Data Sandboxes”—synthetic datasets (like the Wuhan scavenger hunt) that are clean enough for learning but complex enough to remain realistic.
  3. Active Inquiry: Instead of a static lecture, students use AI as a “Co-Pilot” to ask, “What happens if I change the radius?” or “How does the map change if I weight the deaths by age?” This shifts the student from a passive consumer to an active investigator.

🎓 Text for SFHEA Application (PSF 2023 Alignment)

When writing the Fellowship application, adapt these paragraphs to address specific Dimensions of Practice:

On Design and Planning (K1, K4, V3)

“In designing my data visualization curriculum, I moved beyond traditional ‘click-along’ tutorials. By integrating AI-generated synthetic datasets—such as a simulated COVID-19 outbreak in Wuhan—I created a high-stakes, inquiry-based ‘scavenger hunt.’ This approach bridges historical theory (John Snow) with contemporary relevance, ensuring that students from diverse technical backgrounds can engage with complex spatial analysis without being sidelined by initial coding barriers.”

On Enhancing the Student Experience (V1, K2, K3)

“I leverage generative AI to provide ‘just-in-time’ support for students. By providing AI-scaffolded boilerplate code for tools like Folium and Pandas, I empower students to move rapidly from data ingestion to spatial critique. This fosters a sense of ‘Digital Fluency,’ where the technology becomes a transparent medium for epidemiological storytelling rather than a hurdle. This methodology acknowledges that my students are future leaders who must interpret data rapidly in professional settings.”

On Leadership and Scholarly Practice (D3.3, V4)

“My leadership in the department involves modeling how AI can be ethically integrated into the classroom. Rather than banning AI, I use it to generate ‘noisy’ data that requires students to apply human judgment. For instance, in the ‘Wuhan Scavenger Hunt’ exercise, students must distinguish between the ‘Source’ and ‘Secondary Transmission’—a task that requires critical thinking that AI cannot yet automate. This develops their evaluative judgment, a core requirement for graduates in an AI-driven workforce.”

🧪 Practical Next Step

To further strengthen your Fellowship claim, you might want to collect Small-Scale Evaluation Data. Draft a 3-question “Student Perception Survey” to measure how much this “Scavenger Hunt” approach improved their confidence in spatial analysis.

Mentoring, Assessment and the impact of AI on them

This is an excellent expansion for your Senior Fellowship (SFHEA) application. By integrating Assessment & Feedback and Academic Tutoring, you move from “how I teach” to “how I ensure quality and support the whole student.”

Here is the reflective paper, augmented with these critical pillars of higher education practice.

🏛️ Modern Pedagogical Frameworks: Assessment, Feedback, and Mentoring

1. Assessment: From Description to Criticality

In the context of the John Snow or Wuhan mapping exercises, the assessment must go beyond whether a student can “make a map.” A descriptive approach asks, “Where is the pump?”; a critical approach asks, “How does the choice of spatial parameters influence the public health narrative?”

2. The Feedback Loop: The Sandwich and Actionable Intelligence

Feedback should not be a post-mortem; it should be a roadmap. Using the Sandwich Model, we ensure that even the most rigorous critique remains encouraging and personalized.

Layer Component Description
Top Bun Positive Affirmation Start with what worked (e.g., “Excellent use of the Folium library to handle the synthetic dataset”).
The Filling Constructive Critique Shift from descriptive to critical (e.g., “While the map is accurate, your analysis of ‘community spread’ versus ‘the source’ lacked depth”).
The Sauce Actionable Intelligence Explicitly tell the student how to improve (e.g., “To reach the next grade tier, try integrating a time-series element to show the evolution of the cluster”).
Bottom Bun Encouraging Close Reiterate potential (e.g., “You have a clear talent for spatial storytelling; keep pushing the boundary of your interpretations”).

AI and Pedagogy Tip: AI can assist educators in “tone-checking” their feedback to ensure it is forward-looking and justified, helping to turn a one-sentence comment into a personalized growth plan.

3. Academic Tutoring: Mentoring in the Age of AI

As a Wellbeing Champion, the role of the tutor has evolved. In an era where students can get technical answers from AI 24/7, the human mentor must provide something AI cannot: Empathy, Privacy, and Ethical Signposting.

🎓 SFHEA Pointers: Mapping to PSF 2023

If you are using this for your Senior Fellowship, here is how to map these specific sections to the Dimensions of Practice:

A3: Assess and give feedback to learners (Core Area)

“My assessment strategy intentionally shifts students from descriptive to critical engagement. By providing transparent rubrics and using the Sandwich Model of feedback, I ensure that my comments are not just evaluative but provide Actionable Intelligence. This is supported by a robust moderation process that ensures consistency and quality (V4).”

A4: Support and guide learners (Core Area)

“As an academic tutor, I employ the GROW framework to mentor students, particularly in navigating the ethical complexities of AI in data science. By acting as a Wellbeing Champion, I bridge the gap between academic pressure and student support, ensuring a safe, inclusive environment that respects student privacy while signposting them to broader institutional resources (V1, V2).”

K5: Methods for evaluating the effectiveness of teaching (Knowledge Area)

“I integrate External Feedback and moderation cycles into my teaching practice. This not only ensures the reliability of my marking but also allows me to reflect on how my pedagogical tools—like the Python ‘Scavenger Hunt’—align with the evolving needs of the sector.”

🧪 Do next

Create a one-page “Student Success Guide” that explains the GROW framework and Feedback Sandwich to your students so they understand how they are being supported.

AI for staff (medical school)

This expanded curriculum is designed for a 2-part workshop series or a short lecture course. Since your audience is biomedical scientists and clinicians, the focus remains on browser-based tools that require zero installation and zero code.

🏗️ The Curriculum Overview

Goal: Transition staff from “AI-curious” to “AI-competent” by providing a safe, governed toolkit for research, practice, and data.

Module 1: The Augmented Researcher 📚

Focus: Solving “Information Overload.”

Module 2: The Efficiency-First Clinician 🏥

Focus: Reclaiming the “Administrative Burden.”

Module 3: The No-Code Data Scientist 📊

Focus: “Conversation-to-Graph.”

🛡️ The Foundational “Safety & Ethics” Section

Crucial for UK Medical Schools and NHS Staff

Before they touch a tool, you must cover the “Human-in-the-Loop” principle:

  1. Data Governance: Never upload PII (Personally Identifiable Information) to public AI tools. Only use NHS-approved, enterprise-grade instances for patient data.
  2. The “Hallucination” Check: AI is a “Reasoning Engine,” not a “Knowledge Database.” Always verify dosages and clinical facts against a primary source (e.g., the BNF).
  3. UK Regulation: Briefing on the MHRA classification of AI as a medical device and the NHS AI Supplier Registry (established in early 2026).

🗓️ Proposed Workshop Schedule

Time Activity Key Takeaway
00:00 Intro: The AI Revolution in UK Med AI is a co-pilot, not a replacement.
00:30 Session 1: Literature & Discovery Finding the “Needle in the Haystack.”
01:15 Coffee Break  
01:30 Session 2: Workflow & Admin Ending “Death by Documentation.”
02:15 Session 3: Data & Visualization From Spreadsheet to Insight.
03:00 Ethics & Compliance Panel Staying safe within NHS/Uni guidelines.

“Data Safety Handout” or a “Prompt Cheat Sheet” specifically tailored for these medical tools.

AI prototyping tools

Other tools

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