AI evidence extraction

A case study of solving a complex genetics problem to develop generative AI literacy in health science

Chris Della Vedova, David Randall, Kuan Liung Tan, Timothy J. Barnes, Sarah Davey · Learning Letters · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

7/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.20851/ll.v6.43

Methodology & findings

Study design

Case study with mixed methods: quantitative data collected using 5-point Likert scale surveys administered before and after completion of a scaffolded genetics problem-solving task, combined with evaluation of student assessment performance marks..

Primary method

5-point Likert scale surveys (pre- and post-intervention); correlation analysis between assessment performance and assessor evaluation of student prompting and output analysis. Specific statistical software not mentioned.

Main result

Students reported increased understanding of prompt engineering and greater confidence at engaging with GenAI tools. "Student assessment performance was not impacted through the availability of GenAI, indicating that the assessment integrity or purpose was not compromised." Additionally, "there was a correlation between assessment performance and assessor evaluation of student prompting and output analysis."

Reports effect sizes.

Research paradigm

Mixed methods (quantitative survey + assessment performance evaluation)

Author conclusions

"Health science graduates will encounter careers influenced by GenAI enabled tools. Therefore, students require education and opportunity to develop GenAI literacy skills whilst at university. This case study outlines a strategy for teachers to provide AI literacy in health science courses while maintaining assessment integrity and purpose."

Risk of bias

Single institution case study (limited generalizability); No control group mentioned for comparison; Self-reported perceptions via Likert scales (response bias); Temporal design (pre-post without control) vulnerable to maturation and practice effects; Assessment performance evaluated by assessors with potential knowledge of student GenAI use; Single institution/course case study limits generalizability; Self-reported Likert scale data subject to social desirability bias; Pre-post design without control condition; Selection bias: Single cohort from second-year genetics course (no control group mentioned); Single institution case study: Generalizability concerns; No randomization or control condition explicitly stated; Potential social desirability bias in self-reported Likert scale responses

Open questions raised

The abstract identifies that students need to develop GenAI literacy skills and that health science graduates will encounter GenAI-enabled tools in their careers, suggesting a gap in current curriculum preparation for AI literacy in healthcare education.
The authors identify that "Health science graduates will encounter careers influenced by GenAI enabled tools" and emphasize the need for universities to provide education and opportunity for students to develop GenAI literacy skills.
The abstract identifies that students need to develop GenAI literacy skills such as prompt engineering and critical evaluation of GenAI outputs to support their learning and professional practice. The research indicates a gap in providing systematic educational strategies for AI literacy development in health science courses.

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