curriculum designers

Abstract: In this paper, we explore students' perspectives on incorporating hand drawing to conceptualize usable privacy and security research problems. We surveyed 64 female students who participated in an in-class collaborative hand drawing activity to conceptualize usable privacy and security research problems as part of a group-based student research project on usable privacy and security in an information and computer security course for undergraduate students. Our survey responses suggest that students perceived the drawing exercise positively. That is, they found it helpful in several aspects, enjoyable, and they recommend it to other students taking the course. The students also said that they would like to have such an exercise in other courses, such as Principles of Human-Computer Interaction and Fundamentals of Data Networks. Only three students mentioned downsides of the exercise, namely doubts about their drawing skills. Our results provide useful insights for educators seeking to enhance the student learning experience.

Abstract: What happens after a learner makes an error, and how does the instructor's response shape the next turn? Investigating these exchanges across classrooms requires context-sensitive annotation that is difficult to scale manually. We introduce SLA-LENS, an end-to-end LLM-assisted framework that connects classroom transcripts to structured pedagogical annotations and evidence-linked descriptive analytics. Grounded in interactionist Second Language Acquisition (SLA) theory, its annotation schema captures second language learner errors, pedagogical functions, and uptake or repair while accounting for speaker roles, conversational context, and hierarchical labels. We apply the framework to 40 authentic online L2 Spanish classroom transcripts comprising over 21,000 sentence-level instances. We evaluate proprietary and open-weight LLMs, including task-specific QLoRA adaptation, using expert-reviewed gold annotations, held-out silver data, and metrics sensitive to different types of annotation error. A deterministic reporting layer generates reproducible visualizations of participation, feedback, learner errors, uptake, and temporal interaction patterns, linked to supporting transcript evidence. By making these patterns accessible and inspectable, SLA-LENS supports systematic classroom discourse research, pedagogical reflection, and curriculum design.

Abstract: Problem-solving is central to mathematics education. However, diagnosing where students' reasoning breaks down requires individualized probing and consistent criteria that teachers cannot sustain at classroom scale. We introduce STEP (STage-based Evaluation of Problem-solving skills), co-designed with six mathematics education experts, which operationalizes problem-solving as four stages and eight diagnostic sub-skills. By specifying what evidence to elicit and assess at each stage, STEP enables an LLM pipeline to generate stage-specific questions and rubrics for any math word problem, reducing the manual design of diagnostic materials. An expert review (N=8) assessed the pedagogical validity of the generated materials and informed STEPwise, a teacher-in-the-loop system for diagnostic material generation and process-level assessment. In a classroom study with 5 teachers and 64 students, STEPwise helped teachers and students locate reasoning breakdowns and supported students in reflecting on their reasoning. Together, STEP and STEPwise offer a structured approach to fine-grained diagnosis at classroom scale.

Abstract: Generative AI (GenAI) tools entered higher education classrooms faster than the field was able to study their effects on learning. One concern is that GenAI may displace the critical thinking and AI literacy that students will need after graduation. This paper reports a Design-Based Research pilot of a GenAI-assisted critical thinking framework, in which ChatGPT was used as a thinking partner in an undergraduate research methods and statistics course during Spring 2025 (N = 14). The mixed-methods design combined pre- and post-intervention measures of statistical learning (AASCDM), AI literacy (MAILS), and critical thinking (WGCTA) with instructor field notes, student artifacts, and student-AI interaction logs. Pre-post tests showed gains on every AASCDM dimension and on eight of nine MAILS dimensions, while WGCTA percentiles did not change. Qualitative analysis identified four themes: the ways students positioned the LLM (as answer generator, validator, or co-thinker); the depth of student engagement (procedural vs. conceptual); occasional humanizing of the tool; and the role of curriculum design in shaping each of the prior three. Read together, the findings indicate that one semester of GenAI-assisted instruction can move domain learning and self-reported AI literacy but does not move standardized critical thinking, and that the modal student-LLM relationship is one of validation instead of dialogue. We end with design principles for the next iteration of the framework and implications for research on adaptive and personalized learning.

Abstract: Instructional materials can be passed along to new instructors, yet the instructional reasoning, such as instructional intentions, contextual information, and reflective insights behind their design, is rarely preserved. Traditional Learning Management System (LMS) tools are designed to store instructional artifacts rather than reasoning. To address this issue, we propose Teaching Memory, a design approach that treats instructional reasoning as a primary form of knowledge. We present a design-oriented framework for preserving instructional reasoning in a lightweight manner within instructors' existing workflows, enabling continuity and reuse.

Abstract: In practice-based design courses such as knit yarn design, students must turn visual ideas into feasible material outcomes. This is difficult because creative decisions are tied to yarn properties, stitch structures, machine operation, and limited opportunities for physical sampling. This study presents an integrated pedagogical framework that combines flipped learning, exemplar-based reference, GenAI-assisted visual prototyping, and studio feedback in an undergraduate knit yarn design course. The framework was implemented through a cross-device platform with pre-class micro-videos, formative checks, a curated gallery, and a GenAI-supported ideation module. An exploratory course-based evaluation compared a historical control cohort (N = 12) and an intervention cohort (N = 16), supplemented by questionnaire responses and brief interviews. The findings are interpreted as context-specific indicators rather than confirmatory causal evidence. Exploratory comparisons showed higher scores in creativity thinking, design skills, problem solving, and total course score in the intervention cohort. Student and instructor responses suggested that flipped learning supported studio readiness, while GenAI mainly supported early-stage visual exploration rather than precise technical guidance. Overall, the study offers a practice-based instructional framework for integrating flipped preparation, GenAI-assisted visual prototyping, and studio feedback in design education.

Abstract: As artificial intelligence (AI) rapidly iterates and transforms teaching, learning, and knowledge production, philosophical reflection has become increasingly indispensable to educational debates that remain predominantly shaped by Western intellectual traditions. This article proposes Daoism as an alternative philosophical framework for reimagining AI in education. Through philosophical analysis and textual interpretation of classical Daoist sources, brought into dialogue with contemporary scholarship on AI in education, it examines how the Daoist concepts of "Dao nature," "self-cultivation," and the "Zhenren" address fundamental questions concerning reality, the epistemic aims of education, and ethical action in the AI-mediated era. In doing so, the article diversifies the philosophical voices shaping inquiry into AI and education, enriching the field's conceptual resources for grappling with the philosophical questions AI raises for education and offering a genuinely pluralistic foundation for comparative philosophy of education in the AI era.

Abstract: As AI and data-driven systems pervade practice, there is an imperative for instructors to embed societal impact and ethics content into computing courses. In response, we present the Human-Centered Education for Learning in Information and eXplainable Computing (HELIX) framework for information science programs, organized around three iterative pillars - knowledge building, decision-making, and empowerment - with concrete actions for instructors and students. We applied the framework in a graduate, introductory programming course using readings, algorithmic design activities, and scenario-based reflections. We present a pilot implementation of this framework to examine changes in students' (n=22) knowledge acquisition, decision-making processes, and self-reflection regarding human-centered perspectives in data science. We release an anonymized materials kit (survey, assignments, analysis code) to support adoption. We discuss design tensions (workload, assessment, relevance to diverse information science learners) and provide guidelines for integrating human-centered content without overwhelming technical outcomes. Findings suggest that the HELIX Framework is feasible in information science contexts and future work should use comparative survey assessment to strengthen causal inferences.