“Families are messy”: From Parent-Child Tensions to Family-Centered Design of Smart Home Technologies

2026-04-13 · 1 citations

Smart home technologies have become common in family homes, making even young children inevitable users of these technologies. However, these systems are typically designed for individual adults, creating family tensions and conflicts over children’s access, safety, and appropriate smart home use. To investigate children’s and parents’ individual and joint smart home needs and dynamics, we conducted an in-home study with nine families (children aged 6-11). We identify four key parent-child tensions with smart home technologies, including struggles over parental protection versus children’s autonomy, differing views on technology’s purpose, disagreements over technology-enforced routines, and children’s vulnerability to embedded commercialism. Our work reconceptualizes parental mediation as a process of “tension management” rather than the application of static rules. This research challenges the dominant individual-centric choice architecture in smart home design, calling for a family-centered approach that acknowledges and adapts to the fluid, complex, and negotiated reality of modern family life.

Plan More, Debug Less: Applying Metacognitive Theory to AI-Assisted Programming Education

Lecture notes in computer science · 2025-01-01 · 3 citations

Plan More, Debug Less: Applying Metacognitive Theory to AI-Assisted Programming Education

ArXiv.org · 2025-09-03

The growing adoption of generative AI in education highlights the need to integrate established pedagogical principles into AI-assisted learning environments. This study investigates the potential of metacognitive theory to inform AI-assisted programming education through a hint system designed around the metacognitive phases of planning, monitoring, and evaluation. Upon request, the system can provide three types of AI-generated hints--planning, debugging, and optimization--to guide students at different stages of problem-solving. Through a study with 102 students in an introductory data science programming course, we find that students perceive and engage with planning hints most highly, whereas optimization hints are rarely requested. We observe a consistent association between requesting planning hints and achieving higher grades across question difficulty and student competency. However, when facing harder tasks, students seek additional debugging but not more planning support. These insights contribute to the growing field of AI-assisted programming education by providing empirical evidence on the importance of pedagogical principles in AI-assisted learning.

Seasonal Differences Of Physical Activity, Sleep, And Ecological Momentary Assessment In First-year College Students

Medicine & Science in Sports & Exercise · 2025-09-16

Wearable technology use is rapidly increasing in popularity amongst college students. The ability of wearable devices to allow tracking of physical activity and sleep offers a means to self-monitor and reflect upon daily habits that may impact classroom performance. This study provides individualized real-time feedback on habits including: daily physical activity, nightly sleep, and weekly Ecological Momentary Assessment (EMA) of mood, academic engagement, and social connectivity. We hypothesize that these outcomes vary seasonally and may provide insight into students’ well-being during the first-year acclimation period. PURPOSE: Examine seasonal changes of physical activity, sleep, and EMA scores among first-year college students across fall (F: Sep- Dec) and winter (W: Jan - Apr) semesters. METHODS: 303 first-year college students were recruited over 4 semesters (2F, 2 W). Participants received a wearable device to wear daily and nightly and a mobile app for survey completion over an academic semester. Surveys reflected weekly mental health, academic engagement, social connectivity, and drug and alcohol use. T-tests compared fall vs. winter results. RESULTS: 249 participants (82%) met study compliance requirements (A). First-year students averaged 10,326 daily steps and 403 nightly sleep minutes. No significant differences were observed for average daily steps (p = 0.66) and nightly sleep minutes (p = 0.81) between fall and winter terms (B,C). However, standard deviation in daily step count was significantly lower in winter (p < 0.01). Significant improvement in mood score (G,H) was observed in winter (p < 0.05) with no other significant differences across survey outcomes by season. CONCLUSION: Seasonal differences have little effect on average step and sleep habits of first-year students. In contrast, elevated mood scores suggest additional factors corresponding to student wellness are not tied to seasonal changes in physical activity and sleep. Supported by: This study was funded by the University of Michigan Bioscience Initiative

Substrate Oxidation Does Not Influence Middle Distance Running Performance: A Randomized Controlled Crossover Trial

Nutrients · 2025-08-27

Objective: Recent work has challenged the notion that preferred substrate oxidation is a key determinant of exercise performance. This investigation tested middle-distance running performance, in the fed state, to control for glycogen and exercise-induced hypoglycemia (EIH) confounders. Methods: In a randomized crossover fashion, all while controlling dietary intake, activity, and body weight, recreational distance runners completed either a 5K (n = 15; VO2max: 58.3 ± 6.2 mL/kg/min) or a 10K (n = 15; VO2max: 54.51 ± 5.9 mL/kg/min) middle-distance run after consuming isocaloric low-carbohydrate high-fat (LCHF) and high-carbohydrate low-fat (HCLF) pre-exercise meals. Time trial (TT) performance (sec), carbohydrate/fat substrate oxidation, blood metabolites, heart rate (HR), ratings of perceived exertion (RPE), and subjective fullness and thirst were measured throughout. Results: LCHF pre-exercise nutrition reliably altered substrate oxidation and metabolite profiles compared to HCLF, evidenced by significant increases in fat oxidation (77% higher) and reductions in RER (5% lower), with corresponding shifts in carbohydrate oxidation. Despite distinct preferred substrate oxidation profiles during exercise, the 5 and 10 km TT performances were similar between conditions (p = 0.646/p = 0.118). RER was significantly lower (p = 0.002) after the LCHF condition compared to HCLF. Capillary R-βHB increased modestly after LCHF, while blood glucose increased after HCLF only. The LCHF meal was 35% more filling than the HCLF meal. Preferred substrate oxidation did not significantly modulate middle-distance running performance. Conclusion: This work supports recent findings that substrate oxidation is not a primary determinant of aerobic performance, as previously conceived.

Bridging Gaps Between Student and Expert Evaluations of AI-Generated Programming Hints

2025-07-17 · 1 citations

Generative AI has the potential to enhance education by providing personalized feedback to students at scale. Recent work has proposed techniques to improve AI-generated programming hints and has evaluated their performance based on expert-designed rubrics or student ratings. However, it remains unclear how the rubrics used to design these techniques align with students' perceived helpfulness of hints. In this paper, we systematically study the mismatches in perceived hint quality from students' and experts' perspectives based on the deployment of AI-generated hints in a Python programming course. We analyze scenarios with discrepancies between student and expert evaluations, in particular, where experts rated a hint as high-quality while the student found it unhelpful. We identify key reasons for these discrepancies and classify them into categories, such as hints not accounting for the student's main concern or not considering previous help requests. Finally, we propose and discuss preliminary results on potential methods to bridge these gaps, first by extending the expert-designed quality rubric and then by adapting the hint generation process, e.g., incorporating the student's comments or history. These efforts contribute toward scalable, personalized, and pedagogically sound AI-assisted feedback systems, which are particularly important for high-enrollment educational settings.

Applications of Generative AI to Support Teaching and Learning in Higher Education: A Half-Day Workshop

Communications in computer and information science · 2025-01-01

Learnersourcing: Student-generated Content @ Scale: 3rd Annual Workshop

2025-07-17

Bridging Gaps Between Student and Expert Evaluations of AI-Generated Programming Hints

ArXiv.org · 2025-09-03

Generative AI has the potential to enhance education by providing personalized feedback to students at scale. Recent work has proposed techniques to improve AI-generated programming hints and has evaluated their performance based on expert-designed rubrics or student ratings. However, it remains unclear how the rubrics used to design these techniques align with students' perceived helpfulness of hints. In this paper, we systematically study the mismatches in perceived hint quality from students' and experts' perspectives based on the deployment of AI-generated hints in a Python programming course. We analyze scenarios with discrepancies between student and expert evaluations, in particular, where experts rated a hint as high-quality while the student found it unhelpful. We identify key reasons for these discrepancies and classify them into categories, such as hints not accounting for the student's main concern or not considering previous help requests. Finally, we propose and discuss preliminary results on potential methods to bridge these gaps, first by extending the expert-designed quality rubric and then by adapting the hint generation process, e.g., incorporating the student's comments or history. These efforts contribute toward scalable, personalized, and pedagogically sound AI-assisted feedback systems, which are particularly important for high-enrollment educational settings.

Closing the Loop: An Instructor-in-the-Loop AI Assistance System for Supporting Student Help-Seeking in Programming Education

ArXiv.org · 2025-10-16

Timely and high-quality feedback is essential for effective learning in programming courses; yet, providing such support at scale remains a challenge. While AI-based systems offer scalable and immediate help, their responses can occasionally be inaccurate or insufficient. Human instructors, in contrast, may bring more valuable expertise but are limited in time and availability. To address these limitations, we present a hybrid help framework that integrates AI-generated hints with an escalation mechanism, allowing students to request feedback from instructors when AI support falls short. This design leverages the strengths of AI for scale and responsiveness while reserving instructor effort for moments of greatest need. We deployed this tool in a data science programming course with 82 students. We observe that out of the total 673 AI-generated hints, students rated 146 (22%) as unhelpful. Among those, only 16 (11%) of the cases were escalated to the instructors. A qualitative investigation of instructor responses showed that those feedback instances were incorrect or insufficient roughly half of the time. This finding suggests that when AI support fails, even instructors with expertise may need to pay greater attention to avoid making mistakes. We will publicly release the tool for broader adoption and enable further studies in other classrooms. Our work contributes a practical approach to scaling high-quality support and informs future efforts to effectively integrate AI and humans in education.