Agentic education · Learning science

Actionable companion · Learning design & outcomes

Learning with agency: The impact of AI integration in project preparation in education course on pre-service teachers’ innovativeness, AI anxiety, attitudes, and acceptance

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Adult & professional learning. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

The rapid integration of artificial intelligence (AI) in education has transformed teacher education programs. This study examined the effects of incorporating AI tools into a Project Preparation in Education course for pre-service teachers, focusing on individual innovativeness, AI anxiety, AI attitudes, and AI acceptance. Using a single-group pre-test and post-test design, validated scales were administered to 34 pre-service teachers before and after the intervention. Paired-samples t-tests with Holm–Bonferroni corrections revealed significant improvements in AI attitudes (gₐv = 0.64) and AI acceptance (gₐv = 0.70). Individual innovativeness increased slightly, primarily in opinion leadership subdimension (gₐv = 0.35), whereas AI anxiety showed no significant change (gₐv ≈ 0.05). These medium-to-large gains. underscore the potential of guided and AI-supported instruction to enhance teachers’ readiness for AI integration across the stages of the academic writing process. However, the absence of a control group limits causal inference, and the results should be interpreted as correlational evidence of change. Overall, the study highlights both the promise and boundaries of short-term AI interventions in teacher education, offering implications for curriculum design and professional development in AI-driven learning environments. Supplementary Information The online version contains supplementary material available at 10.1186/s40359-025-03647-3.

What kind of evidence is this?

Supporting AI learning science · Comparative / pre-post study reported

Stage: Adult & professional learning. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This quasi-experimental study employed a single-group, pre-test and post-test design to explore the research questions [81]. This design involves assessing a dependent variable within a single cohort of participants, both before and after the introduction of a specific intervention. A primary limitation of this approach is its lack of a control or comparison group, which makes it prone to numerous threats to internal validity. Consequently, establishing a definitive causal link between the intervention and the outcome is difficult. The reason for choosing a single-group design was that the sample size was insufficient to divide participants into two groups [82]. In other words, we selected a one‑group pretest–posttest design, because course‑level constraints precluded randomization or a concurrent comparison section. First, the course is offered once per term to a single, capstone‑stage cohort, making parallel sections infeasible. Second, assigning some final‑semester students to a “no‑AI” condition would remove access to tools now widely used in academic writing and research support, which raised ethical and equity concerns. Third, running the intervention in a single cohort minimized cross-sectional contamination (students routinely collaborate across sections), a common threat in departmental settings. Considering these constraints, we treat the pre–post contrast as associational evidence of change rather than a definitive causal effect.

Limitations and uncertainty · source passage

This study employed a single-group pre-test and post-test methodological framework within a mandated, semester-long academic course. While this design was necessitated by institutional and ethical limitations that precluded withholding AI-supported instruction, the absence of a comparative group limits the capacity to ascertain causal relationships. Differences identified between pre-test and post-test might be connected to developmental changes, external circumstances (including the dynamic dialogue regarding AI), or the effects of repeated assessments. Given that the same instructor facilitated and assessed the course, instructor-related effects cannot be dismissed. Consequently, the findings should be interpreted as correlational rather than indicative of causation. In subsequent iterations of the course, pre-registration will be implemented, and whenever feasible, a control group or comparative cohort will be incorporated to enhance the robustness of the evaluation.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Use a bounded job-relevant simulation or professional-development task with an expert supervisor. Require a traceable rationale and verification before any real-world action. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Assess independent performance on an unfamiliar simulation, retention after a delay, error severity and total review time. Do not equate a faster AI-assisted response with durable professional competence. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Mustafa Sat. The impact of AI integration in project preparation in education course on pre-service teachers’ innovativeness, AI anxiety, attitudes, and acceptance. BMC Psychology (2025-11-24). https://doi.org/10.1186/s40359-025-03647-3

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.