AI evidence extraction

Generating a Student-Informed Teaching and Learning Conceptual Framework for GenAI in Business Schools: A Case Study

Michael Drummond, Gemma Dale · Journal of University Teaching and Learning Practice · 2026

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

6/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.53761/6jvtax83

Methodology & findings

Study design

Mixed-methods case study with survey of 149 students at a UK business school

Sample

N = 149, 1 group

Primary method

Mixed-methods survey analysis; specific statistical methods and software not detailed in the abstract.

Main result

The study found that "students are highly engaged and recognise the necessity of developing GenAI skills for their future careers" with key areas of interest including "the real-world business applications of AI, ethical considerations, and future technological advancements." Additionally, "a significant concern for students is the desire to use GenAI in their academic work without unintentionally committing academic misconduct."

Reports effect sizes.

Research paradigm

mixed methods / pragmatism

Author conclusions

The authors conclude that "this research contributes an innovative model for applied GenAI learning in business education, adding to the growing body of literature on student AI literacy" and present "a new conceptual framework for integrating GenAI education throughout business degree programmes" that "offers practical guidance for educators, including a scaffolded approach to learning activities and assessments across all undergraduate levels."

Risk of bias

Selection bias: Single institution case study (UK business school) may not be generalizable; Self-selection bias: Students who participated in survey may differ from non-respondents; No control group mentioned: Lacks comparison for evaluating impact of GenAI interventions; Self-reported data: Survey responses subject to response bias; Selection bias: single UK business school case study may not generalize; Potential sampling bias: survey respondents may differ from non-respondents; Self-report bias: student perceptions via survey may not reflect actual behavior; Self-report bias: survey-based feedback; Potential volunteer bias in survey participation

Open questions raised

The paper identifies the need for frameworks and guidance on integrating GenAI into business curricula, and addresses student concerns regarding academic misconduct and ethical use of AI technologies.
The paper addresses gaps in student AI literacy and GenAI integration in business curricula, contributing to the growing body of literature on how to prepare students for AI-integrated workforces
The study identifies gaps in understanding how to integrate GenAI education throughout business degree programmes and the need for practical guidance on scaffolded approaches to learning activities and assessments.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 69%

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