Comparing Student and Generative AI Responses in a Haematological Malignancy Care Plan

School of Pharmacy

About this use case

Students interpret patient notes and apply the key steps of the MMOC process to identify medication-related problems and develop a patient-centred care plan for a case involving haematological malignancies. After completing and self-assessing their own care plan, students use generative AI to produce a response to a similar case and prepare a comparative report evaluating the AI-generated response against their own work and relevant clinical guidance.

Learning outcomes:

Gather and integrate information about the medicine, patient and condition to identify potential management options.
Create a medication management plan that incorporates established clinical guidelines and a comprehensive review of medicine-related problems.

To help students
- apply their knowledge of haematological malignancies,
- develop a complete patient-specific medication management plan, and
- critically compare their own clinical reasoning with a generative AI response.
1. Provide students with patient notes and a structured MMOC template.
2. Guide students through the key stages of identifying and assessing medication-related problems.
3. Ask students to complete an initial self-assessment before viewing or generating an AI response.
4. Provide clear instructions limiting generative AI use to Question 2 of the comparative report.
5. Require students to submit the AI tool used, the prompts entered and the full AI-generated response.
6. Provide examples of suitable prompts and APA-style referencing for generative AI use.
7. Reinforce the need to check AI-generated recommendations against established clinical guidelines.
- Improved ability to identify medicine-related problems in complex clinical cases.
- Stronger patient-centred medication management planning.
- Greater confidence in applying clinical guidelines.
  Improved self-assessment and reflective practice.
- Greater awareness of the limitations and lack of specificity in generative AI responses.
- Stronger critical evaluation of AI-generated clinical information.

Try it out

This use case requires an easy level of effort to implement.

- Provide students with a clinical case and a structured care-planning template.
- Ask students to develop and self-assess their own patient-specific management plan before using generative AI on a similar case.
- Restrict AI use to a clearly defined section of the task and require students to submit their prompts, the AI output and a comparative report.
- Include a second self-assessment to support reflection on the similarities, differences and limitations identified.
- Ask students to complete their own clinical reasoning before using generative AI so that the AI response does not replace the original learning process.
- Require full documentation of AI use and provide examples of acceptable prompts and referencing.
- Emphasise that AI-generated recommendations may be general or incomplete and must be checked against current clinical guidelines and patient-specific information.
Seeing a developmental arc through sampling over time
Visible learning is implemented through a development arc, where students first complete an independent patient management plan, then self-assess their performance, compare their reasoning with a generative AI response, and reflect on how they would improve their approach in a future case. This makes changes in clinical reasoning, judgement and self-evaluation visible across the assessment.

Supporting resources

Here are some additional resources that you can browse to help you from TeachHQ:

Learn about AI at your own pace? Enrol in online courses via myDevelopment. Alternatively, you can join ongoing chats about AI and webinars.

Sidebar

Unit: PHR3041	Condition: Individual
Class size: 80–90	Typology: Written
AI approach: Using and applying AI tools effectively	AI statement: Guided use
Visibility of Learning: Seeing a developmental arc through sampling over time

About the educator

Validated by

Amelia Nathania, Education Designer, Education Excellence