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AI evidence extraction

A Panel Report on the Implications of Artificial Intelligence for Academic Knowledge Work

Aizhan Tursunbayeva, Maarten Renkema, Andy Charlwood, Christian-Andreas Schumann, Emelie Schwill · Journal of the Association for Information Systems · 2026

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

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Methodology & findings

Study design

World Café symposium with qualitative discussion and thematic analysis of lived experiences from academics attending the European Academy of Management 2025 Conference

Primary method

Qualitative analysis of World Café symposium discussions; specific statistical or analytical methods not detailed in abstract

Main result

The study found that "the impact of AI in academia is shaped by factors across different organizational levels, including individual AI literacy, institutional governance approaches, and disciplinary norms." Additionally, "participants recognized numerous opportunities to enhance teaching and research productivity, while simultaneously expressing concern about potential ethical and professional risks."

Reports effect sizes.

Research paradigm

Interpretivist/qualitative

Author conclusions

The authors propose that "a research agenda for Information System scholars that emphasizes examining human–AI collaboration, designing responsible and trustworthy AI tools, comparing adoption patterns across contexts, and exploring the career implications of AI, particularly for early-career academics" is needed to advance understanding of AI's impact on academic work.

Risk of bias

Selection bias: participants self-selected to attend symposium; Participation bias: only academics attending European Academy of Management 2025 Conference; Facilitator bias: World Café format may influence responses; Limited geographic/disciplinary representation: Participants drawn from a single conference may not represent all academic disciplines or regions; Potential disciplinary representation bias

Limitations

  • The paper acknowledges "a notable lack of empirical insight into how academics themselves experience these transformations" prior to this study, suggesting that the current work, while addressing this gap through qualitative methods, may be limited in scope to symposium participants and lacks systematic empirical measurement.

Open questions raised

  • The authors identify the need for research on:
  • human-AI collaboration in academic contexts, (2) designing responsible and trustworthy AI tools, (3) comparing AI adoption patterns across different contexts and disciplines, (4) exploring career implications of AI adoption, particularly for early-career academics, and (5) understanding how institutional governance and disciplinary norms shape AI implementation
Data: not_statedCode: not_statedExtracted from: pdf

Explore related topics

Responsible AI UseMulti-DomainQualitative

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