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.

9/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

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; Geographic bias: limited to European conference attendees; Selection bias: Participants self-selected to attend a symposium on AI in academia, likely representing those with interest or concern about the topic; Social desirability bias: Participants in a World Café setting may moderate responses based on group dynamics; Limited geographic/disciplinary representation: Participants drawn from a single conference may not represent all academic disciplines or regions; Self-selection bias of symposium participants; Single conference venue (European Academy of Management 2025); Potential disciplinary representation bias; Volunteer sampling from World Café format

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: (1) 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
The authors identify the need for research on: human–AI collaboration, design of responsible and trustworthy AI tools, comparative adoption patterns across contexts, and career implications of AI for early-career academics. They note the existing "notable lack of empirical insight into how academics themselves experience these transformations and how such developments affect their daily work."
Human–AI collaboration in academic settings; design of responsible and trustworthy AI tools for academia; comparative analysis of AI adoption patterns across different disciplinary and institutional contexts; career implications of AI, particularly for early-career academics

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