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The AI first suggested a project, followed by a reflective analysis by the student. I told it that I didn't want to mark 200 essays, so it came up with the following, which is more reasonable.

Assessment Proposal: "Object-Oriented Programming Challenge with Code Annotations and Quizzes"

Assessment Overview:

Students will complete a programming challenge with built-in tasks that assess their understanding of fundamental programming concepts (LO1), object-oriented design (LO3), and good coding practices (LO4). Instead of writing a reflective essay, they will annotate their code directly and complete short auto-graded quizzes that ask them to explain their design decisions and the role of key code segments.

This assessment includes:

1. Part A: Coding Challenge (60%)

2. Part B: Code Annotations and Auto-Graded Quizzes (40%)

Part A: Coding Challenge (60%)

Task:

Students will develop a simple object-oriented application (similar to the mini-project proposed earlier), such as a Student Management System or a Simple Banking System.

The application must demonstrate:

Imperative Programming Concepts: Use of variables, conditionals, loops, and functions/methods (LO1).

Object-Oriented Principles: Creation of classes, objects, and methods, demonstrating encapsulation, modularity, and good design (LO3).

Good Coding Practices: Consistent code structure, meaningful variable/method names, and clear documentation within the code (LO4).

Automated Marking:

Test Cases: Students' submissions will be run through a suite of automated test cases that assess the correctness of their code. For example, the system checks if classes and methods work correctly, inputs are handled, and expected outputs are produced.

Code Quality Metrics: Automated tools such as static analysis (e.g., pylint for Python) can evaluate code for readability, consistency, and adherence to best practices like appropriate variable naming and code layout (LO4).

This part of the assessment ensures that students develop working code while removing the need for manual grading of 200 coding submissions.

Part B: Code Annotations and Auto-Graded Quizzes (40%)

Task:

Code Annotations: Students will be required to add concise inline comments within their code (max. 20–30 words per section), explaining key choices they made, such as:

Why they used a particular loop or conditional statement (LO1).

How their object-oriented design works (e.g., the purpose of each class, how encapsulation is used) (LO3).

What steps they took to ensure good coding practices (LO4).

Auto-Graded Quizzes: Alongside their coding submission, students will complete a series of short, auto-graded quizzes that ask them about their code structure and design choices. For example:

1. Multiple-Choice Questions: “Which of the following statements best describes the use of encapsulation in your code?”

2. Short Answer Questions (Auto-Graded with Keywords): “Briefly explain how you ensured modularity in your program.”

These quizzes can be tied to specific parts of the code, meaning students will engage with the reasoning behind their implementation. For example, a question might ask, "What is the role of the withdraw() method in your application?"

Use of AI:

Students are encouraged to use AI for assistance, but rather than writing a full reflection, they will log their AI interactions as part of the quiz (in a structured format). For instance:

Auto-Graded Response: "What did you use AI for in your project? (Select all that apply)" – Options: Debugging, Finding Resources, Learning Syntax, etc.

Short Answer with Keywords: “Summarise how you applied feedback from AI tools to debug your code."

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AI-Assisted Research Essay - students will write a 2,500-word essay on a given topic and use AI to help assist with the research process. A further 500 words will be dedicated to explaining how AI was used as part of the research process and evaluating its effectiveness.

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Sometimes we ask students to communicate their research and project findings to different audiences in different formats, e.g. digital poster, leaflet, video or social media post. It'd be great to explore how they could use GenAI's multimodal capabilities to do this (and/or critique it).

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Assessment Type: Anatomy Spotter Exam with an AI-enhanced Reflection Component

The spotter exam will test students’ identification of anatomical structures and functions using stations (either physical models, images, or digital diagrams). After completing the spotter, students will write a reflective piece on how they applied their anatomical knowledge and how AI tools (like ChatGPT) could enhance their study techniques or clinical applications.

This was intersting as I didn't think this was an assessment style that could really benefit from an AI component, or would really be at risk of students using AI to answer it

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1. Group Campaign Design and Pitch Presentation (50%)

Task: In groups of 4-5, students will be tasked with designing a detailed Integrated Marketing Communications campaign for a selected brand/product.

Pitch Presentation: Groups will present their campaign as if pitching to the brand's senior management team. This will involve a 10-minute presentation, followed by a Q&A.

2. Reflective Report (50%)

Each group member will submit an individual reflective report (1,500 words) evaluating the campaign.

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I asked for assessment for PGT small animal surgery module. Put in our module LOs. It's actually came up with something very similar to what we currently do (reflective case reports) but with an idea of how to integrate AI, which would in fact be quite doable. ie they must submit an AI-generated log where they detail their use of AI (prompt used, results gained) and critically reflect on how they verified the information from reputable sources like peer-reviewed veterinary journals. This actuallycomes up with a good way of incorporating AI into what we currently do already

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Essay: Discuss the social issue in relation to the selected theory, applying sociological concepts critically.

Counter-Arguments: Acknowledge alternative viewpoints and critically assess their limitations.

Conclusion: Summarise the key findings of your analysis and state the broader implications for understanding the social issue.

Suggested a reflective Appendix (500 words):

Describe how AI was used in the research and drafting stages.

Critically evaluate the AI-generated suggestions (e.g., Was the AI accurate in sourcing information? Were there any biases or oversights?).

Reflect on the ethical implications of using AI for academic writing.

Explain how you refined AI-generated content to align with your own critical thinking and voice.

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Nice ending to an essay question Critical Reflection (20%)

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Nice ending to an essay question Critical Reflection (20%)

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A written reflection (500 words) on the ethical and practical implications of using AI in their anatomical analysis.

Students must reflect on:

The strengths and weaknesses of the AI-generated insights.

How AI shaped their thinking or analysis.

The role of AI in anatomy education—can it replace or complement traditional learning methods?

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Describe the process of searching for (including using AI tools), summarising and synthesising the literature for writing up a literature review on a chosen topic – 500 words.  Then write up the literature review – 2000 words, Finally, write up a reflection and evaluation of the whole process - 500 words.

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Pre-sessional English course: Reflective Defence Component

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Pre-sessional English course: Reflective Defence Component

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Students have a 10-minute post-essay interview to defend the process and views. I said this is resource heavy. the GPT revised it by including a reflective commentary or including reflective Qs in the 1-1 tutorials. It offers the benefits of being scalable and no extra teaching load

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The AI provided some nice tips for writing good MCQs:

Designing Effective MCQs for Medical Students

To ensure the questions are challenging and relevant, here are some key principles to keep in mind:

1. Case-Based Questions: Present questions in the context of clinical cases. This encourages students to apply theoretical knowledge to practical scenarios, rather than just recalling facts.

2. Distractor Plausibility: Make sure the incorrect options (distractors) are plausible, requiring students to critically think through their options rather than dismissing obviously wrong answers.

3. Cognitive Levels: Use a mix of cognitive levels (e.g., from basic recall to analysis and application) to ensure a well-rounded assessment. The Blooms Taxonomy framework is helpful here:

Recall: What structure/condition is this?

Comprehension: Why does this condition occur?

Application: What would you expect in this case?

4. Length and Focus: Keep questions focused on a single learning point, with enough context to make the case realistic. Avoid overly complex wording to ensure clarity.

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This Level 4 History assessment asks students to choose a source from a provided list and write a 1,500-word critical evaluation of its reliability, bias, and usefulness in understanding a specific historical event or period. Students may use AI tools to generate preliminary summaries or clarify context, but must disclose their AI usage and critically compare AI-generated content with their own analysis. The assessment emphasizes critical thinking, historical analysis, and engagement with AI in a transparent and meaningful way, while maintaining academic integrity through reflection on AI’s limitations.