Bodong Chen

Bodong Chen

As generative AI rapidly integrates into educational infrastructures worldwide, it transforms how knowledge gets created, validated, and shared, yet current discourse inadequately addresses its implications as epistemic infrastructure mediating teaching and learning. This paper investigates how AI systems function as epistemic infrastructures in education and their impact on human epistemic agency. Adopting a situated cognition perspective and following a value-sensitive design approach, the study conducts a technical investigation of two representative AI systems in educational settings, analyzing their impact on teacher practice across three dimensions: affordances for skilled epistemic actions, support for epistemic sensitivity, and implications for long-term habit formation. The analysis reveals that current AI systems inadequately support teachers' skilled epistemic actions, insufficiently foster epistemic sensitivity, and potentially cultivate problematic habits that prioritize efficiency over epistemic agency. To address these challenges, the paper recommends recognizing the infrastructural transformation occurring in education, developing AI environments that stimulate skilled actions while upholding epistemic norms, and involving educators in AI design processes -- recommendations aimed at fostering AI integration that aligns with core educational values and maintains human epistemic agency.

Bodong Chen, Haiyi Zhu

To support ethical considerations and system integrity in learning analytics, this paper introduces two cases of applying the Value Sensitive Design methodology to learning analytics design. The first study applied two methods of Value Sensitive Design, namely stakeholder analysis and value analysis, to a conceptual investigation of an existing learning analytics tool. This investigation uncovered a number of values and value tensions, leading to design trade-offs to be considered in future tool refinements. The second study holistically applied Value Sensitive Design to the design of a recommendation system for the Wikipedia WikiProjects. To proactively consider values among stakeholders, we derived a multi-stage design process that included literature analysis, empirical investigations, prototype development, community engagement, iterative testing and refinement, and continuous evaluation. By reporting on these two cases, this paper responds to a need of practical means to support ethical considerations and human values in learning analytics systems. These two cases demonstrate that Value Sensitive Design could be a viable approach for balancing a wide range of human values, which tend to encompass and surpass ethical issues, in learning analytics design.