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Effects of an AI-supported inquiry model on AI literacy and authentic performance: A quasi-experimental study with preservice teachers

◐ medium · 2026-06-03ai-literacy, faculty-development, generative-ai, efficacy-study, higher-ed

Synthesis: Effects of an AI-supported inquiry model on AI literacy and authentic performance: A quasi-experimental study with preservice teachers

Key Findings

  • A 10-week quasi-experimental study with 95 preservice teachers in an educational research methods course compared two intact classes (experimental n = 52; comparison n = 43). The groups were equivalent at baseline, with no statistically significant pre-intervention differences in age (M = 20.38 vs 20.05), gender (12:40 vs 5:38 male:female), grade level, major, or AI-literacy pretest scores (M = 79.46 vs 80.33).
  • The QUEST+AI model structures AI-supported inquiry in five phases: Question, Understand, Engage, Solve, and Teach.
  • The experimental group showed higher overall AI Literacy, with small but meaningful gains concentrated in applying AI, AI-supported problem solving, and emotion regulation during AI use (partial η² ≈ .05–.09). Within-group gains ran from M = 79.46 (SD = 12.22) at pretest to M = 84.94 (SD = 8.27) at posttest (t(51) = 3.66, p < .001), whereas the comparison group showed no statistically reliable change.
  • No clear group differences were found for more concept-focused or evaluative dimensions of AI literacy (concepts, detection, ethics, creation, and persuasion).
  • The experimental group earned higher scores on the final research proposal (partial η² = .137), indicating a moderate advantage in authentic performance: after covariate adjustment, adjusted means were 84.14 vs 80.72 (F(1, 90) = 14.24, p < .001), a difference of 3.42 points (95% CI [1.62, 5.22]).

    • Study Design & Method

    Grounded in Deweyan Inquiry and the Practical Inquiry model, the study examined the effects of QUEST+AI, an AI-supported inquiry model built around five phases: Question, Understand, Engage, Solve, and Teach. Both groups received the same in-class instruction, but the experimental group completed two QUEST+AI cycles with coached generative AI use, whereas the comparison group completed conventional homework. Outcomes included a multidimensional AI literacy measure and a capstone research proposal scored with a common rubric. Within-group change was assessed with paired-samples t-tests, and group differences with ANCOVA controlling for pretest AI literacy, gender, and grade. The coached cycles embedded prompt logs and verification routines that made AI-supported decisions visible, and the same inquiry routines mapped directly onto the rubric dimensions used to score the proposals (problem framing, synthesis quality, methodological coherence, and argumentation).

    Implications for AI in Education

    The findings suggest that structured AI-supported inquiry can strengthen applied and self-regulatory aspects of AI literacy while improving discipline-relevant performance — evidence that coached GenAI use embedded in authentic academic work outperforms conventional homework for developing practical AI competence. The moderate effect on research proposal quality indicates that AI-supported inquiry does not trade away disciplinary learning for tool proficiency. Notably, these gains were achieved without expanding lecture time: the experimental group received no additional AI-literacy-focused direct instruction, which the authors suggest explains the absence of change on concept-heavy and evaluative scales. For Teacher AI Competency and Faculty Development efforts, the model offers a concrete, phase-structured template for integrating GenAI into coursework, with brief targeted activities (concept refreshers with retrieval checks, verify-and-trace exercises, rubric-scored ethics cases) recommended to address the dimensions the model alone did not shift. The pattern of results marks this as an Research Methods AIED whose design logic favors pedagogical specificity over mere tool availability.

    Limitations

    The authors acknowledge several limitations: nonrandom group assignment within a single institution, brief exposure, a modest sample size, several short subscales with modest reliability, self-reported literacy outcomes, and rubric-based scoring for performance. These features increase ecological validity but limit causal inference and may reduce sensitivity to change; stronger controls and objective measures — such as randomized or crossover designs, longer interventions with follow-up measures, and performance-based assessments triangulated with protocol-adherence data — would improve robustness.

    Connected Concepts

  • Teacher AI Competency
  • AI Literacy
  • Research Methods AIED
  • Faculty Development
  • Professional Training
  • K 12 AI Education
  • Instructional Design
  • Higher Ed

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    • Citation

    Cao, D., Yan, Y., Xiong, A., & Wicks, D. (2026). Effects of an AI-supported inquiry model on AI literacy and authentic performance: A quasi-experimental study with preservice teachers.