Adult learners raised data-practice concerns that did not map onto the Community of Inquiry framework, including not knowing what data is collected and where it goes¶

Claim · All claims
Evidence · 2 studies · 2 qualitative r2 · q2
Currency · no dated study recorded · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Guideline G1 (data practices) was derived mainly from student concerns about not knowing what data is collected, how it is stored, and who can access it, and was not assigned a Community of Inquiry component. → g1-data-1 q2 i? Students' institutional trust in data handling does not extend to third-party educational tools. → g1-data-2

Evidence¶

g1-data-1¶

Jennifer M. Reddig, Glen R. Smith Jr., Sanaz Ahmadzadeh Siyahrood, Wesley G. Morris, Yoojin Bae, Kaitlyn Crutcher, John Kos, Rahul K. Dass, Jinho Kim, Momin Naushad Siddiqui, Daniel Weitekamp, Ploy Thajchayapong, Sandeep Kakar, Alex Endert, Scott Crossley, Min Kyu Kim, Chris Dede, Ashok Goel, and Christopher J. MacLellan. 2026. Guidelines for Designing AI Technologies to Support Adult Learning. In Designing Interactive Systems Conference (DIS '26), Singapore, Singapore. ACM, New York, NY, USA. https://doi.org/10.1145/3800645.3813102

q2 · i? · qualitative · r2

In the guidelines section, the authors report that G1 was "derived mainly from student concerns about not knowing what data is being collected, how it is stored, and who can access it", and explain they did not force it into the Community of Inquiry framework because it would not increase cognitive, social, or teaching presence.

"This guideline was derived mainly from student concerns about not knowing what data is being collected, how it is stored, and who can access it."

g1-data-2¶

Jennifer M. Reddig, Glen R. Smith Jr., Sanaz Ahmadzadeh Siyahrood, Wesley G. Morris, Yoojin Bae, Kaitlyn Crutcher, John Kos, Rahul K. Dass, Jinho Kim, Momin Naushad Siddiqui, Daniel Weitekamp, Ploy Thajchayapong, Sandeep Kakar, Alex Endert, Scott Crossley, Min Kyu Kim, Chris Dede, Ashok Goel, and Christopher J. MacLellan. 2026. Guidelines for Designing AI Technologies to Support Adult Learning. In Designing Interactive Systems Conference (DIS '26), Singapore, Singapore. ACM, New York, NY, USA. https://doi.org/10.1145/3800645.3813102

q2 · i? · qualitative · r2

The article reports, citing prior work, that students' "implicit trust in their institution to handle data ethically" does not extend to third-party educational tools, supporting the guideline's emphasis on transparency about data sent to third-party vendors.

"Higher education students have an implicit trust in their institution to handle data ethically [74], but that trust does not extend to data collected by a third-party educational tool [69]."

Discussion¶

Related Claims¶

• Community adults entering an AI education session held broad, generalized concerns about AI, including distrust of self-regulation and privacy worries — related
• Privacy protections lag behind reputational concerns: institutions rarely consider implications of capturing audio-visual and behavioral data from private spaces, and AI algorithms are seldom transparent about error rates — related
• Students raised four categories of concern about potential misuse of MMLA data: judgment, privacy, career impact, and academic integrity — related
• Students held misconceptions about how MMLA data was collected and processed, and most relied on guesswork when explaining the analysis pipeline — related