Actionable companion · Learning design & outcomes
Learning with agency: Investigating the Impact of the AI-Supported 5E (AI-s5E) Instructional Model on Spatial Ability
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.
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): Secondary school. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
Abstract Improving students’ spatial abilities is an important goal in education. Spatial ability is a skill needed in many fields, such as science, mathematics, engineering, and architecture. Since this ability can be improved through training, this study adopted a quasi-experimental design to investigate the effects of an artificial intelligence-supported 5E (AI-s5E) instructional model on students’ spatial visualization, spatial relationships, and spatial orientation performances that explain their spatial abilities. A total of 43 students from two classes at a secondary school in western Turkey were recruited to participate in this study. One of the classes was the experimental group (f = 23), which adopted the AI-s5E approach, and the other class was the control group (f = 20), which adopted the traditional teaching model. The results showed that the integration of the AI-s5E instructional approach into education improved students’ spatial abilities and sub-dimensions. In light of the findings, it can be recommended that AI applications, which have a positive and significant impact on spatial skills, can be integrated into teachers’ lessons and even included in curriculum programs. Keywords: spatial ability (SA), artificial intelligence (AI), 5E instructional
What kind of evidence is this?
Supporting AI learning science · Comparative / pre-post study reported
Stage: Secondary school. 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
In this study, a quasi-experimental design consisting of a pre-test and post-test was used. Quasi-experimental designs are preferred in cases where it is not possible to form the classes through random assignment to experimental and control groups by the researchers [62]. Two fifth-grade classes were selected for our study, and these classes are educated in the same state secondary school. One of the selected classes was determined as the experimental group and the other as the control group. With the students in the experimental group, the AI-s5E teaching model was applied. The students in the control group were taught in accordance with the course book, and no intervention was made. Our study was carried out in the spring term of the 2024 academic year. One of the researchers in this study is a mathematics teacher in a public school and has experience with AI-supported tools. This researcher carried out the teaching with the experimental and control groups in this study. At the beginning of this study, a Spatial Visualization Test (SVT), a Spatial Relations Test (SRT), and a Spatial Orientation Test (SOT) were applied to the experimental and control groups as a pre-test. In addition, a Spatial Visualization Test (SVT), a Spatial Relations Test (SRT), and a Spatial Orientation Test (SOT) were administered to both groups at the end of the instruction. The research design is provided in Figure 1.
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
- 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.
- Days 16–30 · Design. Ask learners to attempt a problem unaided, compare an agent hint, explain a revision and verify the final reasoning against a trusted source. Make the permitted use explicit for each assignment. Name an accountable owner and set the baseline, success threshold and budget before implementation.
- 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.
- Days 61–90 · Decide. Measure unaided transfer and delayed retention, calibration of confidence and the ability to identify an incorrect AI explanation. Record whether help-seeking becomes answer copying. 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
Nejla Gürefe; Gülfem Sarpkaya Aktaş; Hava Öksüz. Investigating the Impact of the AI-Supported 5E (AI-s5E) Instructional Model on Spatial Ability. Behavioral Sciences (2024-08-06). https://doi.org/10.3390/bs14080682
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.