Ying-Tian Zhan, Xin Su, Bei-He Hui et al. (4 authors) • Semantic Scholar
Finding: Teachers' AI use improves student learning outcomes in higher education, mediated by learning engagement (cognitive, behavioral, emotional) and moderated by teacher AI literacy.
Why it matters: Relevant to AI's impact on learning and skill acquisition, and offers empirical grounding for how instructor AI integration shapes student engagement—useful context for teaching about AI's role in education.
Jargon: *Moderated mediation*: a statistical model where the indirect effect of X on Y through a mediator varies depending on a moderator variable. *Self-determination theory*: motivation theory distinguishing intrinsic vs. extrinsic drivers of engagement.
Adelfa C. Silor, Faith Stephanny C. Silor • Semantic Scholar
Finding: A quasi-experimental study (n=98 high school students) testing a Dewey-inspired "TMRC" pedagogical model for AI integration in education, finding significant gains in engagement, critical thinking, and knowledge retention over traditional instruction.
Why it matters: Touches on AI's impact on learning, learner agency, and collaborative inquiry—relevant to how AI tools reshape educational practice and skill acquisition.
Method: Quasi-experimental design with traditional vs. TMRC-integrated groups, using both quantitative performance metrics and qualitative insights.
Note: The paper's core contribution is a named pedagogical framework rather than insight into HCI, creativity, team dynamics, or behavioral science specifically. The Dewey framing is conceptually interesting but the empirical execution (98 students, one school context, self-reported engagement) limits generalizability. Moderate relevance for AI-in-education angle only.
Finding: This dissertation proposes integrating cognitive biases, decision-making styles, and behavioral signals into recommender systems to better align recommendations with how humans actually make choices, rather than relying on static personality trait models like OCEAN.
Why it matters: The behavioral science angle (cognitive biases, contextual decision-making, bounded rationality) overlaps with the professor's interests in behavioral science and persuasive technology, and touches on AI impact on human behavior, but the recommender systems application is fairly distant from HCI, creativity, teams, or goal-setting.
Method: Uses the RetailRocket e-commerce dataset to infer cognitive attributes from behavioral interaction data, combined with LLM-based reasoning frameworks and human-centered user studies.
Jargon: *OCEAN framework* = Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism); *cognitive attributes* = decision-making tendencies like risk aversion or satisficing vs. maximizing.
Zuleykha Gasimova, Martin Harry Turpin, Alexander C. Walker et al. (5 authors) • PsyArXiv
Finding: People consistently judge for-profit companies as less socially beneficial than non-profits, and this anti-profit bias persists even when participants explicitly agree that profit-seeking can produce societal benefits—suggesting a disconnect between abstract beliefs and concrete judgments.
Why it matters: Marginally relevant to behavioral science and cognitive biases (specifically attitude-behavior gaps and motivated reasoning), but has no meaningful connection to HCI, creativity, teams, learning, or AI.
⭐ Includes replication ⚠️ No control group mentioned | Limited statistical detail in abstract
Sadriana Ayu, Rizal Arizaldy Ramly, M. Mustaji et al. (7 authors) • Semantic Scholar
Finding: Project-based learning integrated with Technology Acceptance Model significantly improves self-regulated learning, with perceived usefulness and ease of use partially mediating this effect.
Why it matters: Touches on goal-setting and learning sciences, but the focus is narrow educational technology adoption measurement rather than insight into mechanisms of learning, creativity, or behavior change.