LMAP-1A is an introductory module for all first-year medical students. The essay is assessed by a 5-minute group-based audio podcast (with written transcript), element of peer evaluation and an in person question and answer session that was introduced as a response to the wide spread use of AI. 

Initially the module was assessed by the pdodcast, peer review element and a commentary. Initial simulations by the LMAP team have showed that IA tools (with appropriate prompting) can produce an adequate basic script for the assessment. The commentary component of the assessment can also be partially addressed by more mainstream generative AI tools. Accordingly, only the peer evaluation is immune from generative AI manipulation. 

The podcast was felt to be a valuable assessment that allowed students to develop relevant skills that graduates need. There was not a suitable alternative to replace it. It was decided that the best option is to replace the individual written commentary with an in-person individual question session. 

A lot of work has been done to diversify assessments for Years 1 and 2. LMAP-1A incorporates a more innovative assessment type. The second-year continuation of the module (LMAP-1B) focuses on more exam-based assessments. The podcast therefore offers a more creative alternative which is appropriate for the type of skills that are assessed. 

There are five aspects to preparing students to produce a podcast: 

• Initial briefing which sets out the task and the requirements, i.e. what to include in the transcript and how to do it, go through an indicative podcast structure etc. The accompanying live session will also take a use-case based guide that shows students where and how generative AI may be used to support their work.
• Library support on how to search and review the evidence.
• A resource provided by the central college communications team that supports the students how to actually do the podcast in terms of recording, interview techniques etc.
• Topic-specific learning that builds on the core learning developed over the course of the year.
• Check-in office hours style appointments that students have with their topic lead (who is the person responsible for one of the 5 areas students explore) 

Students will be explicitly briefed about what is considered appropriate and inappropriate use of AI for the purpose of this assessment. This briefing currently consists of the following points: 

• Explanation that AI models are powerful and can be an effective way to check the quality of written work, prompt new ideas, or generate simplified explanations of complex topics to support your learning.    
• A warning that submitting work and assessments created by someone or something else, as if it was your own, is plagiarism and is a form of cheating and this includes AI-generated content.  

The briefing will be developed further in the coming year. 

Students (approx. 360) rank their preferences for each of five LMAP topics which are: 

• Mental health
• Physical activity
• Nutrition 
• Sleep
• Health and wealth 

Students are subsequently allocated in random groups of five students by a best fit algorithm. Many of the students will not have worked with each other prior to this assessment. The students have freedom in what to explore within each area as long as it is linked to the theme of behaviour change, inequalities and population health. 

After the podcasts have been submitted, students conduct a structured reflection activity that serves as preparation for peer feedback. They do this in their original tutor groups – in which they were placed for the two terms preceding their elective podcast groups. This reflection focuses on what they learnt and what they did not learn. Then they are given a link to the feedback form which is based on three criteria – professionalism, attitude and contribution. 

The assessments take place in person over two consecutive days and are scheduled for 20-minute sessions (that includes the podcast listening) with a five-minute interval for changing groups and conciliating marks. The total evaluation score for a student will be divided by that group’s average evaluation score. That value will be multiplied by the examiner’s grade for the podcast to get the final individual score.