Feedback Fruits tool; evaluating its effectiveness in supporting Advanced Clinical Practice students formative assessment

Lavery, J and Vann, M (2025) Feedback Fruits tool; evaluating its effectiveness in supporting Advanced Clinical Practice students formative assessment. In: LJMU Student at the Heart Conference, 18th Jun - 19th Jun 2025, Liverpool, United Kingdom.

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Abstract

LJMU MSc Advanced Clinical Practice (ACP) programmes begin with a core module titled ‘Underpinning ACP’. The module begins the process of self directed personal development and growth for apprenitceship and non apprenticeship students. The module maps to the NHS England (2017) multi professional framework for advancing practice and the knowledge skills and behaviours for ACP (IfATE, 2018) . Students join the programme from a diverse range of multiprofessional backgrounds studying at level 7. The ACP module team opted to use ‘Feedback fruits’ an artificial intelligence (AI) tool as an opportunity to standardise feedback and enhance formative assessment processes for all students. The integration of Feedback Fruits aligns with LJMU’s Strategy 2030, particularly its focus on leveraging digital tools to enhance teaching, learning, and the student experience. The formative assessment aimed to maximise student feedback by using a multilayer approach using a sequence of AI, self-assessment, and academic tutor feedback. The total number of students who used this tool was 50. A short survey to evaluate experiences was interesting albeit with a low response rate of 6 students. Students gave a rating of 3 stars for usefulness of the AI tool, curiously in terms of feedback students ranked feedback from lecturers as their top preference method, self-assessment against grade descriptors as second and least preference was automated feedback. Staff views were also varied and despite acknowledging potential benefits, academics felt this tool was cumbersome to use within the canvas site and added an additional layer of complexity to the formative assessment marking. The pilot therefore demonstrated a commitment to understanding student needs and preferences, with a clear takeaway that personalised, academic tutor feedback is the most valued method. Moving forward, the team feel prioritising human feedback while incrementally integrating AI tools could still enhance both formative assessment processes and student satisfaction.

Item Type:	Conference or Workshop Item (Poster)
Subjects:	L Education > LB Theory and practice of education > LB2300 Higher Education R Medicine > RT Nursing
Divisions:	Nursing and Advanced Practice
Publisher:	LJMU
Date of acceptance:	18 June 2025
Date of first compliant Open Access:	30 June 2025
Date Deposited:	30 Jun 2025 14:30
Last Modified:	30 Jun 2025 14:31
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26668

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