New
Communications of the Association for Information Systems (2026) AI Processed Human Approved
A Panel Report on the Implications of Artificial Intelligence for Academic Knowledge Work
This study explores how artificial intelligence is transforming academic knowledge work across teaching, research, and academic service. Utilizing an explorative World Café methodology during the EURAM 2025 Conference, the authors captured lived experiences, reflections, and practices from diverse academic participants.
Problem
While AI adoption in higher education is rapidly accelerating, existing research remains largely conceptual, technology-centric, and fragmented across isolated academic activities. Consequently, there is a lack of empirical insight into how academics holistically experience AI transformations and adapt their daily work roles.
Outcome
- Academics actively utilize AI tools like ChatGPT and Copilot primarily for research, followed by teaching and administrative service tasks.
- Key opportunities include automating routine teaching preparation, supporting scholarly writing for non-native English speakers, and streamlining grant application workflows.
- Participants raised major concerns regarding ethical risks, AI hallucinations, the degradation of peer-review quality, and the potential erosion of critical learning and research skills.
- AI impacts are mediated by individual AI literacy, institutional policies, and disciplinary norms, necessitating proactive university governance.
- A research agenda is outlined for Information Systems scholars emphasizing human-AI collaboration, responsible AI design, and the career impacts on early-career academics.
- Key opportunities include automating routine teaching preparation, supporting scholarly writing for non-native English speakers, and streamlining grant application workflows.
- Participants raised major concerns regarding ethical risks, AI hallucinations, the degradation of peer-review quality, and the potential erosion of critical learning and research skills.
- AI impacts are mediated by individual AI literacy, institutional policies, and disciplinary norms, necessitating proactive university governance.
- A research agenda is outlined for Information Systems scholars emphasizing human-AI collaboration, responsible AI design, and the career impacts on early-career academics.
What it means for you
- CIO / IT Executive: On Monday morning, initiate a cross-departmental working group meeting to inventory existing AI tool usage across teaching, research, and administrative functions, identifying commonalities and immediate risks based on the 'Key Findings' regarding AI hallucinations and ethical concerns.
- IT Manager: On Monday morning, schedule a technical assessment to evaluate the security protocols and data privacy implications of the most commonly used AI tools (e.g., ChatGPT, Copilot) currently being utilized by academic staff, referencing the 'Key Findings' on ethical risks.
- Business Strategist: On Monday morning, begin drafting a preliminary AI integration roadmap for academic workflows by identifying 2-3 specific routine teaching preparation or scholarly writing tasks (as per 'Key Findings') that could be piloted for AI automation within the next quarter.
- Researcher: On Monday morning, experiment with using an AI tool like ChatGPT or Copilot to draft an initial outline for a grant application section (referencing 'Key Findings' on streamlining grant workflows) or to refine a paragraph for clarity if English is not your primary language, noting both the efficiencies gained and any potential 'hallucinations' or quality degradation.
- Policymaker: On Monday morning, schedule a review of current institutional policies related to academic integrity and technology use, specifically flagging areas that need to be updated to address the 'Key Findings' on AI hallucinations, the degradation of peer-review quality, and the erosion of critical learning skills.
Transcript
Host: Welcome to A.I.S. Insights — powered by Living Knowledge. I'm your host, Anna Ivy Summers. Today, we're diving into a fascinating new study titled "A Panel Report on the Implications of Artificial Intelligence for Academic Knowledge Work." Joining me is our expert analyst, Alex Ian Sutherland. Alex, what is this study set out to explore?
Expert: Thanks, Anna. At its core, this study looks at how artificial intelligence is transforming the daily reality of knowledge workers. The researchers specifically focused on higher education, examining how AI tools are reshaping three core pillars of academic work: teaching, research, and professional service.
Host: That sounds timely! AI in education is certainly making headlines everywhere. But why did the authors feel the need to conduct this specific study right now? What's the big problem they're addressing?
Expert: While AI adoption has exploded across universities, most existing research has been purely conceptual, overly technical, or fragmented. For instance, people would study AI in teaching separately from AI in research. We lacked empirical insight into how academics holistically experience these changes across their entire workload, and how it impacts their professional identity and daily practices.
Host: I see. So how did the researchers gather these real-world insights from actual academics?
Expert: They used an explorative qualitative methodology called a World Café during the EURAM 2025 conference in Florence. They brought together nineteen diverse academics—ranging from PhD candidates to full professors across multiple countries—and had them participate in rotating, small-group brainstorming sessions focused on teaching, research, and service, followed by surveys and a plenary synthesis.
Host: That sounds like a great way to capture genuine, lived experiences. So, Alex, what did they actually find? Where are academics using AI the most?
Expert: Surprisingly, participants reported using AI most frequently for research, followed by teaching, and lastly for academic service. In research, tools like ChatGPT, Copilot, and Perplexity are acting as augmented research assistants—helping with literature reviews, coding, and drafting. Non-native English speakers highlighted how AI democratizes academic writing by leveling the playing field in language fluency.
Host: That's a huge benefit for global collaboration. Were there concerns raised about research, though?
Expert: Absolutely. Participants warned about AI hallucinations, loss of analytical depth, and the risk that over-relying on AI could erode critical thinking skills. There's a real fear of researchers becoming detached from their own data.
Host: What about in teaching and administrative service? How is AI impacting those areas?
Expert: In teaching, AI acts as a virtual assistant that creates syllabi and rubrics, but it forces a fundamental role shift: educators are moving from knowledge transmitters to learning facilitators. Traditional homework assignments are becoming obsolete, requiring completely new assessment models. In academic service, while AI helps draft grant proposals, there's severe concern over AI-generated peer reviews. Many reported receiving generic, low-quality reviews, leading to a situation where AI tools are essentially evaluating text written by other AI tools.
Host: That raises some big red flags around trust and quality. Why does all of this matter for the broader business world beyond university campuses?
Expert: Academia is really a preview of what's happening to knowledge workers in every industry. First, the human role across corporate sectors is shifting from execution to curation and oversight. Second, there's a critical threat to junior talent development. If AI automates all entry-level tasks, junior professionals lose the routine practice needed to build deep expertise and critical judgment.
Host: That’s a crucial point for business leaders thinking about workforce pipelines. What should organizations take away from this?
Expert: Organizations can't just passively let AI adopt itself bottom-up. They need proactive governance, continuous training in AI literacy, and work redesign models that preserve human critical thinking. AI offers immense productivity gains, but without intentional structures, it risks diluting output quality, eroding core skills, and creating systemic trust issues.
Host: A powerful reminder that technology implementation is always a human management challenge. Alex, thank you so much for breaking down this study for us.
Expert: My pleasure, Anna.
Host: And thank you to our listeners for tuning in to A.I.S. Insights — powered by Living Knowledge. Join us next time as we continue to explore the frontiers of technology, work, and business strategy.
Expert: Thanks, Anna. At its core, this study looks at how artificial intelligence is transforming the daily reality of knowledge workers. The researchers specifically focused on higher education, examining how AI tools are reshaping three core pillars of academic work: teaching, research, and professional service.
Host: That sounds timely! AI in education is certainly making headlines everywhere. But why did the authors feel the need to conduct this specific study right now? What's the big problem they're addressing?
Expert: While AI adoption has exploded across universities, most existing research has been purely conceptual, overly technical, or fragmented. For instance, people would study AI in teaching separately from AI in research. We lacked empirical insight into how academics holistically experience these changes across their entire workload, and how it impacts their professional identity and daily practices.
Host: I see. So how did the researchers gather these real-world insights from actual academics?
Expert: They used an explorative qualitative methodology called a World Café during the EURAM 2025 conference in Florence. They brought together nineteen diverse academics—ranging from PhD candidates to full professors across multiple countries—and had them participate in rotating, small-group brainstorming sessions focused on teaching, research, and service, followed by surveys and a plenary synthesis.
Host: That sounds like a great way to capture genuine, lived experiences. So, Alex, what did they actually find? Where are academics using AI the most?
Expert: Surprisingly, participants reported using AI most frequently for research, followed by teaching, and lastly for academic service. In research, tools like ChatGPT, Copilot, and Perplexity are acting as augmented research assistants—helping with literature reviews, coding, and drafting. Non-native English speakers highlighted how AI democratizes academic writing by leveling the playing field in language fluency.
Host: That's a huge benefit for global collaboration. Were there concerns raised about research, though?
Expert: Absolutely. Participants warned about AI hallucinations, loss of analytical depth, and the risk that over-relying on AI could erode critical thinking skills. There's a real fear of researchers becoming detached from their own data.
Host: What about in teaching and administrative service? How is AI impacting those areas?
Expert: In teaching, AI acts as a virtual assistant that creates syllabi and rubrics, but it forces a fundamental role shift: educators are moving from knowledge transmitters to learning facilitators. Traditional homework assignments are becoming obsolete, requiring completely new assessment models. In academic service, while AI helps draft grant proposals, there's severe concern over AI-generated peer reviews. Many reported receiving generic, low-quality reviews, leading to a situation where AI tools are essentially evaluating text written by other AI tools.
Host: That raises some big red flags around trust and quality. Why does all of this matter for the broader business world beyond university campuses?
Expert: Academia is really a preview of what's happening to knowledge workers in every industry. First, the human role across corporate sectors is shifting from execution to curation and oversight. Second, there's a critical threat to junior talent development. If AI automates all entry-level tasks, junior professionals lose the routine practice needed to build deep expertise and critical judgment.
Host: That’s a crucial point for business leaders thinking about workforce pipelines. What should organizations take away from this?
Expert: Organizations can't just passively let AI adopt itself bottom-up. They need proactive governance, continuous training in AI literacy, and work redesign models that preserve human critical thinking. AI offers immense productivity gains, but without intentional structures, it risks diluting output quality, eroding core skills, and creating systemic trust issues.
Host: A powerful reminder that technology implementation is always a human management challenge. Alex, thank you so much for breaking down this study for us.
Expert: My pleasure, Anna.
Host: And thank you to our listeners for tuning in to A.I.S. Insights — powered by Living Knowledge. Join us next time as we continue to explore the frontiers of technology, work, and business strategy.