Han Zhang, Bianca Done, Stefanie Schneider et al. (5 authors) • PsyArXiv
Finding: This scoping review examines whether individual cognitive abilities (especially working memory capacity and attentional control) predict how well humans can "re-enter the loop" when AI systems require human input after automated task handling; evidence is thin and mostly from automated driving studies.
Why it matters: Directly relevant to AI impact on human collaboration and work—specifically the question of human oversight, cognitive readiness, and individual differences in human-AI teaming contexts that matter for HCI and behavioral science.
Method: Scoping review of 13 studies meeting inclusion criteria; notably sparse evidence base with no studies on generative AI systems.
Jargon: *Loop re-entry* = the moment when a human must take back control or provide input after an AI has been handling a task autonomously; *attentional control* = ability to direct and sustain focus while resisting distraction.
Finding: Narrative literature review arguing that sociocognitive counseling techniques (self-efficacy, modeling, goal-setting, cognitive restructuring) help students recover from academic failure.
Why it matters: Touches on goal-setting and behavior change topics, but at a surface, applied-counseling level with no novel empirical contribution.
Method: Narrative literature review via Google Scholar, PubMed, PsycINFO — low methodological rigor, no systematic selection criteria described.
Finding: Displaying judgment certainty in social networks reorganizes who people choose to observe (toward more certain sources) but does not improve accuracy—and can actually hurt performance by increasing exposure to highly certain but *incorrect* judgments.
Why it matters: Directly relevant to collective intelligence, team decision-making, and behavioral science around information cues and cognitive biases—specifically how confidence signals mislead information seeking, a key issue in collaborative sense-making and belief updating.
Method: 8-session online experiment with 273 participants forming costly directed observation networks over 5 belief-update rounds on true/false news headlines; compared 6 predictive models of belief revision.
Jargon: *DeGroot model* = a weighted averaging model of social belief updating where people iteratively average their neighbors' opinions; *directed observation networks* = participants pay a cost to choose whose judgments to view; *reconstructed net scores* = accuracy metric accounting for the task's payoff structure.
⚠️ No control group mentioned | Single study (not replicated) | Limited statistical detail in abstract
Cambridge Social Decision-Making Lab, Liana Cheetham, Kelly Wong Kar Yee et al. (4 authors) • PsyArXiv
Finding: A cognitive inoculation intervention (forewarning + exposure to weakened deepfake examples) significantly improved participants' ability to detect deepfake videos (d = 0.625) by increasing both motivation to resist and self-reported detection ability.
Why it matters: Marginally relevant as a behavior change / persuasive technology study — the inoculation mechanism (prebunking) connects to choice architecture and nudge-adjacent interventions, and deepfakes relate to AI's societal impact, but the focus is narrow (misinformation detection) and unlikely to be directly teachable in HCI or behavioral design courses.
Method: Pre-registered between-subjects experiment (N = 297) with a video-based inoculation intervention followed by a 10-video real/deepfake classification task.
Jargon: *Cognitive inoculation/prebunking* — exposing people to weakened forms of manipulative content to build psychological resistance, analogous to vaccine logic; *response bias* — tendency to favor one classification response regardless of accuracy.
Arthur Chin Haeng Lau, R. Previdelli • Semantic Scholar
Finding: Proposes a framework for designing and implementing game-based learning (GBL) in comparative anatomy education, covering pedagogical justification, game mechanics selection, and curricular integration.
Why it matters: Has some relevance to games/gamification and learning sciences, but is narrowly focused on veterinary anatomy education rather than the broader HCI, behavioral science, or gamification design principles the user cares about.
Jargon: *Game-based learning (GBL)*: using game mechanics and structures as instructional tools; *serious games*: games designed primarily for educational or training purposes rather than entertainment.
Ying-Tian Zhan, Xin Su, Bei-He Hui et al. (4 authors) • Semantic Scholar
Finding: Teachers' AI use improves student learning outcomes by increasing cognitive, behavioral, and emotional engagement, with the effect amplified when teachers have higher AI literacy.
Why it matters: Touches on AI impact on learning and skill acquisition, but is narrowly focused on teacher-side instructional use in Chinese higher education rather than HCI, design, or behavioral science mechanisms.
Shuxian Jin, Vanessa Clemens, Laura C. Hoenig et al. (10 authors) • PsyArXiv
Finding: This paper provides a practical guide for conducting cross-cultural behavioral economic games online, arguing they reveal cross-cultural differences in human behavior mechanisms.
Why it matters: Marginally relevant to behavioral science and decision-making research methodology, but focused on cross-cultural research infrastructure rather than HCI, design, or behavior change applications.
Finding: Gamifying an auditory statistical learning task improved its psychometric reliability in children with reading difficulties, and performance on the gamified task correlated with reading fluency.
Why it matters: Touches on gamification design and learning sciences, but the core focus is clinical psychometrics and reading disability—too specialized and peripheral to the professor's primary interests.
⭐ Includes replication ⚠️ No control group mentioned
Ni Made Citariani, K. Agustini, I. K. Suartama et al. (6 authors) • Semantic Scholar
Finding: An AR framework combining brain-based learning principles and local cultural values (Tri Kaya Parisudha) improved prosocial behavior, critical thinking, and communication in early childhood settings using a quasi-experimental design.
Why it matters: Tangentially relevant to learning sciences and educational technology design, but the focus on early childhood character education in a culturally specific Indonesian context is far from the professor's core interests.
Method: ADDIE instructional design model with expert validity review and quasi-experimental classroom evaluation.
⚠️ No control group mentioned | Single study (not replicated) | Limited statistical detail in abstract