SIDLIT 2026

Instructional designers constantly face challenges at their job. Given the interdisciplinary nature of the field and the considerable variances from position to position, it is vital to understand the instructional design (ID) professionals’ own experiences. This paper reports voices from novice ID professionals on the essential requirements of their ID work, and essential ID professional characteristics. From analyzing interviews with seven ID professionals from a variety of work contexts, the study reveals that the variety of key responsibilities fall onto four dimensions on contradictory ends, indicating four sets of characteristics that ID professionals need to learn to balance well. The findings present the four sets of characteristics in a spider web form with an instructional design at the center, promoting a shift to taking a holistic approach when developing competencies. This paper bears practical implications for aspiring and novice instructional designers, faculty who provide education for ID students, and employers who aim to look for the best fits for the positions, making contributions to the education of instructional designers, hiring and further professional development of ID professionals.

As generative AI becomes increasingly embedded in online and blended education, instructors face a difficult question: how can learning be supported and assessed when students can quickly generate polished answers, explanations, lesson materials, and multimedia products? Much of the current conversation focuses on academic integrity, prompt writing, tool adoption, or AI-use policies. While these issues are important, they do not fully address a deeper instructional question: what does meaningful learning look like when AI-generated output becomes part of the learning process? This work-in-progress session begins with a dialectical autoethnographic inquiry into a 24-turn interaction between the presenter and a generative AI system. Although the interaction began with a personally meaningful problem related to IRA planning, the focus of the analysis is not financial decision-making. Rather, the episode is used as a situated case for examining how learning unfolds within a human–AI–artifact system. Preliminary analysis suggests that AI-generated outputs should not be treated as final answers, neutral tools, or authoritative explanations. Instead, they function as epistemically unstable learning materials that require human verification, revision, justification, and synthesis. Building from this analysis, the session translates the emerging theoretical insight into practical strategies for online and blended teaching. The presenter will introduce assignment and assessment structures that foreground process evidence rather than only final products, including prompt archives, revision logs, delta reports, AI feedback loops, AI defense activities, peer process audits, and reflective synthesis prompts. These strategies are designed to help instructors evaluate how students define problems, examine AI-generated output, verify information, revise their thinking, justify decisions, and produce a defensible final synthesis. Participants will leave with a conceptual vocabulary for understanding generative AI-mediated learning and a set of adaptable strategies for designing assignments that make student judgment visible. The session is intended for educators, instructional technologists, online program leaders, and educational technology researchers interested in moving beyond AI-use compliance toward more rigorous, reflective, and assessable forms of AI-supported learning.

Artificial Intelligence (AI) is rapidly transforming higher education, yet many instructional implementations position students as passive consumers of AI-generated content rather than active participants in the learning process. Emerging research suggests that excessive reliance on AI tools may reduce learner autonomy, weaken self-regulated learning behaviors, and contribute to what scholars describe as “metacognitive laziness.” While AI-powered systems can improve academic performance and provide adaptive support, little research has examined instructional approaches that require students to critically engage with and improve AI-generated outputs.


This study proposes a human-AI co-design instructional model in which students actively evaluate, critique, and revise AI-generated content rather than simply accepting AI responses. Using a convergent mixed-methods research design, the study will investigate how this approach influences learner agency, cognitive load, and academic performance among undergraduate students in higher education. Approximately 40–60 students will participate in a quasi-experimental comparison between a traditional instructional environment and a human-AI co-design learning environment. Quantitative data will be collected through pre- and post-assessments, learner agency surveys, and cognitive load measurements, while qualitative data will be gathered through interviews, written reflections, and learning management system interaction logs. 


The study seeks to address three important gaps in current literature: the limited examination of students as active evaluators of AI outputs, the lack of understanding regarding cognitive load in AI co-design environments, and the unresolved tension between AI-driven personalization and learner autonomy. Findings are expected to provide practical guidance for educators, instructional designers, and higher education institutions seeking to integrate AI in ways that enhance critical thinking, learner engagement, and meaningful learning outcomes.

In this presentation I will share the results of a recently completed study of graduate students' lived experience of a pedagogy of care in an online asynchronous learning environment. The results are compared to the experiences of undergraduate students reported in the scholarly literature.  The study focused exclusively on graduate students and aims to current research on the application of Noddings’ (2013) model of care to online contexts. Using an interpretative phenomenological approach, we analyzed participant-generated documents within an online, asynchronous course. Findings revealed few differences between graduate and undergraduate experiences of care pedagogy. Results support two proposed extensions to Noddings’ framework: Robinson et al.’s (2020) division of modeling to two contexts, course design and teaching, and Byrd et al.’s (2025) concept of anticipation. Although limited in generalizability, this exploratory qualitative study contributes to understanding an understudied population and reinforces evidence that care-centered pedagogy can mitigate negative aspects of asynchronous learning, such as feelings of isolation and disempowerment (Burke & Lamar, 2021), which are associated with reduced learning success.