Barriers that Programming Instructors Face While Performing Emergency Pedagogical Design to Shape Student-AI Interactions with Generative AI Tools

TL;DR

This study investigates how computing instructors respond to the rapid rise of GenAI by framing their adaptive efforts as emergency pedagogical design—reactive, indirect, and under time pressure with limited visibility into student-AI usage. Through 13 semi-structured interviews and a 169-person survey, the authors identify five pervasive barriers: fragmented buy-in, policy crosswinds, implementation challenges, assessment misfit, and lack of resources. They characterize emergency pedagogical design as a distinct design setting for HCI and offer open research questions and practical recommendations for researchers, institutions, and funders to bolster instructor-led adaptation. The work highlights equity concerns and the need for scalable, supported approaches to align course materials, policies, and assessments with GenAI-enabled learning environments.

Abstract

Generative AI (GenAI) tools are increasingly pervasive, pushing instructors to redesign how students use GenAI tools in coursework. We conceptualize this work as emergency pedagogical design: reactive, indirect efforts by instructors to shape student-AI interactions without control over commercial interfaces. To understand practices of lead users conducting emergency pedagogical design, we conducted interviews (n=13) and a survey (n=169) of computing instructors. These instructors repeatedly encountered five barriers: fragmented buy-in for revising courses; policy crosswinds from non-prescriptive institutional guidance; implementation challenges as instructors attempt interventions; assessment misfit as student-AI interactions are only partially visible to instructors; and lack of resources, including time, staffing, and paid tool access. We use these findings to present emergency pedagogical design as a distinct design setting for HCI and outline recommendations for HCI researchers, academic institutions, and organizations to effectively support instructors in adapting courses to GenAI.

Figures (5)

• Figure 1: Although a strong majority of survey respondents considered themselves open to adopting GenAI technologies (a), only a minority felt their colleagues were equally open (b). However, only a few instructors perceived colleague resistance to GenAI as a barrier (c), suggesting that ambivalence was more common than direct opposition. Instructors responded on a 5-point scale. The number of responses for each option is displayed within each bar.
• Figure 2: Surveyed instructors felt motivation to update their curriculum in response to student usage of GenAI (a). Institutional constraints had mixed perceived impact (b). Instructors responded on a 5-point scale. The number of responses for each option is displayed within each bar.
• Figure 3: Surveyed instructors felt it was important to integrate GenAI tools into their curriculum (a), but fewer instructors reported actually doing so (b). Instructors responded on a 5-point scale. The number of responses for each option is displayed within each bar.
• Figure 4: A substantial number of our survey respondents expressed that their current workloads restricted their time to learn and integrate GenAI (a), and that their institutions did not support this work (b). Instructors responded on a 5-point scale. The number of responses for each option is displayed within each bar.
• Figure 5: Responses to the question "In your opinion, what kinds of support would be most helpful to address faculty resistance to teaching GenAI?" Survey respondents were allowed to choose up to three options from a pre-defined list. Eight most common responses shown, with counts displayed to the right of each bar.