Jianhong Li, Vaishali Agarwal, Ebtesam Abdullah A. Alzeiby et al. (5 authors) • Semantic Scholar
Finding: A structural model tested on 307 students across six countries shows that perceived AI support, trust, and adaptive personalization drive collaborative engagement, which sequentially enhances self-monitoring and metacognitive reflection, ultimately improving academic achievement, creativity, and ethical problem-solving.
Why it matters: Directly addresses AI's impact on learning, self-regulation, and creativity in educational contexts — all core themes for a professor teaching HCI, behavioral science, and learning sciences.
Method: Serial mediation analysis using structural equation modeling across a 6-country student sample.
Jargon: *Self-Regulated Learning (SRL)* — learners actively monitoring and controlling their own cognitive processes; *Metacognitive reflection* — thinking about one's own thinking and learning strategies; *Hybrid Intelligence* — human-AI systems that complement each other's strengths.
Mercede Erfanian, Prince Teye, Marco Heimann et al. (5 authors) • PsyArXiv
Finding: Default nudges don't reduce cognitive effort uniformly during complex decisions; instead, they shift *when* mental effort occurs (more during decision phase, less during recall), suggesting nudge acceptance involves active evaluation rather than passive effort-saving.
Why it matters: Directly challenges a core assumption in choice architecture/behavioral science—that defaults work by reducing cognitive load—with physiological evidence, relevant for teaching nudge theory and behavioral economics.
Method: Pupillometry (pupil dilation as proxy for cognitive arousal) combined with working-memory load manipulation (digit-span) in a lab setting; pupil responses are a more sensitive measure than behavioral outcomes alone.
Jargon: *Default nudge* = pre-selected option that shapes choice without restricting alternatives; *pupil-linked arousal* = changes in pupil diameter tracking mental effort/arousal; *task-evoked pupil response* = pupil dilation locked to specific task phases; *encoding-locked/decision-locked/recall-locked* = pupil responses time-anchored to different task phases.
⚠️ Small sample (n=56) | No control group mentioned | Single study (not replicated)
Finding: Psychology students and staff across three UK universities express concerns about GenAI in assessment, identifying three core themes: human-machine ambiguity, overreliance weakening cognition, and erosion of the social contract between educators and learners.
Why it matters: Directly addresses AI's impact on learning, skill acquisition, and human collaboration in educational contexts—particularly the cognitive dependency and trust dynamics that are central to understanding AI's effects on human capital.
Method: Qualitative focus groups with thematic analysis across three institutions, including participant reactions to human vs. AI-generated feedback examples.
Jargon: *Social contract* — the implicit mutual obligations and trust between students and educators that define the educational relationship; *GenAI* — generative AI tools like ChatGPT used to produce text-based outputs.
⚠️ Very small sample (n=11) | No control group mentioned | Single study (not replicated) | Convenience sample (e.g., MTurk, students) | Limited statistical detail in abstract
Dylan Richards, Kaitlin Woolley, Adam Fetterman et al. (5 authors) • PsyArXiv
Finding: Scoping review of 25 studies showing the new year as a temporal landmark increases initial motivation and engagement in health behavior change, though evidence for long-term maintenance is weak.
Why it matters: Directly relevant to behavior change design, specifically the "fresh start effect" (temporal landmarks as motivational resets), which connects to choice architecture, nudge theory, and goal-setting research.
Method: PRISMA-ScR scoping review across PsycINFO, PubMed, and Web of Science; 27 independent studies synthesized narratively.
Jargon: *Temporal landmark* — a psychologically meaningful time marker (e.g., new year, birthday, Monday) that creates a "fresh start" mental reset, separating past failures from future attempts.
Finding: GenAI-supported collaborative project-based language learning produced greater gains in English achievement and learner autonomy (especially motivation and self-regulation) compared to traditional instruction among EFL undergraduates, though over-reliance on AI outputs emerged as a concern.
Why it matters: The AI dependence/autonomy tension is relevant to the professor's interest in AI's impact on human skills, but the domain (EFL language learning) is narrow and peripheral to HCI, creativity, teams, or behavioral science.
Method: Explanatory sequential mixed-methods with linear mixed-effects modelling across experimental and control groups (n=53).
Jargon: *PBLL* = Project-Based Language Learning; *EFL* = English as a Foreign Language; *GenAI-mediated scaffolding* = AI tools providing structured support to guide learner performance.
⭐ Longitudinal design ⚠️ Self-reported data only | Single study (not replicated) | Convenience sample (e.g., MTurk, students)
Shengtian Wu, Cara Dillon, Adam Lockwood • PsyArXiv
Finding: Proposes an "augmented triad framework" where AI acts as a partner in school psychology consultation, supporting problem-solving phases like data organization, reflection, and decision-making while raising ethical concerns.
Why it matters: Tangentially relevant to AI's impact on professional work and human-AI collaboration, but narrowly focused on school psychology consultation rather than HCI, creativity, teams, or behavioral science broadly.
Finding: Proposes an AI-enabled perceptual alignment framework for manufacturing quality inspection that redistributes perceptual judgment from human operators to algorithmic systems, framed through impression integration theory.
Why it matters: The human-to-AI authority shift in decision-making touches on AI's impact on human cognition and work, but the application domain (industrial quality inspection, golf ball manufacturing) is too far from the professor's core interests.