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AI evidence extraction

Future research recommendations for transforming higher education with generative AI

Thomas K. F. Chiu · Computers and Education Artificial Intelligence · 2023

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

7/10
Relevance
0/4
Quality (LMQS)
E
Evidence
385
Citations
13.65
FWCI
Top 10%
Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1016/j.caeai.2023.100197

Methodology & findings

Study design

Qualitative study combining systematic literature review and thematic analysis.

Sample

N = 51, 2 groups

Primary method

Thematic analysis was used to identify patterns in qualitative data. Specific software tools, coding procedures, or statistical software are not mentioned in the abstract.

Main result

The study findings suggest that "future higher education should be transformed to train students to be future-ready for employment in a society powered by GenAI." The research identified that students recommend "new learning outcomes—skills in learning and teaching with GenAI, AI literacy—and emphasize the significance of interdisciplinarity and maker learning, with assessment focusing on in-class and hands-on activities."

Reports effect sizes.

Research paradigm

Qualitative interpretivism

Author conclusions

The authors conclude that "GenAI's impact on learning outcomes, pedagogy, and assessment is crucial for reforming and advancing the workforce" and recommend "six future research directions – competence for future workforce and its self-assessment measures, AI literacy or competency measures, new literacies and their relationships, interdisciplinary teaching, Innovative pedagogies and their evaluation, new assessment and its acceptance."

Risk of bias

Selection bias: Sample of 51 students from three research-intensive universities may not be representative of all higher education contexts; Limited geographic diversity: Unclear if universities are from single country or region; Potential response bias: Students who volunteered may have different perspectives on GenAI than non-participants; Researcher bias: Thematic analysis relies on researcher interpretation without reported inter-coder reliability or validity checks; Potential self-selection bias in student participation; Single perspective (student only) without faculty or administrator input; Selection bias: Sample limited to students from three research-intensive universities only; Potential selection bias in student recruitment/participation; Limited geographic or institutional diversity; No mention of researcher bias mitigation in thematic analysis

Open questions raised

  • Competence measures for future workforce and self-assessment tools
  • AI literacy or competency measurement frameworks
  • New literacies and their interrelationships
  • Interdisciplinary teaching approaches
  • Evaluation of innovative pedagogies with GenAI
  • New assessment methods and stakeholder acceptance
Data: not_statedCode: not_statedExtracted from: pdfAgreement 63%

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