Actionable companion · Cognitive load & attention

Learning with agency: Persuasion, pride and prejudice: factors associated with students’ resistance to a social robot’s incorrect mathematical solution

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): Secondary school. Verify the participant population and task before transferring the findings.

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

Abstract This exploratory study investigates how secondary school students resist or align with a social robot that challenges their reasoning in a curriculum-based mathematics task. Twenty-two students collaborated with a Furhat robot on a geometry problem whose three parts were familiar from class. In the critical part, all students produced task-relevant evidence for the correct solution, while the robot proposed and defended an incorrect solution through argument-based persuasion, without deliberately invoking peripheral persuasive cues. We examined resistance to the robot’s informational social influence in two ways. First, as an endpoint outcome, 14/22 students aligned with the robot: three immediately after dissent and eleven after a persuasion debate, while eight resisted. Second, as a process index, we introduce an approach to quantify early expressed resistance: students’ responses within the first two persuasion debate exchanges were descriptively coherent with endpoint outcomes. Rather than testing causal or predictive relations among endpoint outcomes, early resistance, and questionnaire or interaction measures, we interpret observed patterns, prior theory, and interactional design to formulate seven hypotheses for future research: Students’ resistance may be stronger with higher self-perceived competence (1), with behavioural patterns consistent with self-efficacy when cognitive demands are higher (2), and with stronger task-directed attention before conflict, suggesting that early engagement may provide descriptive markers of later resistance (3). Resistance may be weaker among students reporting more frequent conversational AI/LLM use and stronger preferences for human-like robot attributes (4). For the robot, incorrect arguments may appear more objective and credible when they are delivered through neutral wording, tone, and facial expressions without explicit uncertainty cues (5), and when the arguments, while incorrect, are heuristically plausible (6). Finally, students who aligned may be more vulnerable to later robot influence (7), since uncertainty and trust in the robot’s information appeared to rise together during debate until alignment. These hypotheses suggest that calibrated robot behaviours (e.g., signalling uncertainty when the information is less reliable) may help sustain students’ resilience and self-efficacy when they engage with persuasive yet fallible educational robots. Keywords: cognitive dissonance, conformity, educational robots, informational trust, persuasion, self-consistency, self-efficacy, social influence

What kind of evidence is this?

Embodied learning agents · Survey / observational study

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

Twenty-two participants (19 males and 3 females, average age 16.5 ± 0.51 years) volunteered in June 2024, during the final week of the spring term, after their maths teacher distributed an invitation to interact with an educational robot after class hours, to avoid interfering with teaching or coercing participation. The teacher was not present during the experiment and was not informed about individual students’ performance on the maths task. As described further in Section 6.2, all students completed an informed consent form explaining their right to abstain or withdraw without consequences. Students were told that their participation would help test the robot’s ability to collaboratively solve maths problems, and that their performance would not be communicated to their teacher.

Limitations and uncertainty · source passage

The main limitations of the study are the small sample size, the gender imbalance, and the recruitment of all students from the same class, which limit generalisability. At the same time, the small and relatively homogeneous cohort enabled an in-depth exploratory analysis and ensured comparable classroom exposure to the theorem targeted in the dissent. Teacher-guided task selection and the interactional evidence reported in Table 6 further indicate that all students had task-relevant access to the correct solution path, although this should not be treated as an independent diagnostic test.

Design the system before the pilot

Compare the embodied interaction with an equivalent human-led or screen-based activity. Separate the effects of novelty and social presence from learning. Review physical access, sensory needs and adult supervision.

Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.
Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

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

Pablo González-Oliveras; Olov Engwall; Ali Reza Majlesi. Persuasion, pride and prejudice: factors associated with students’ resistance to a social robot’s incorrect mathematical solution. Frontiers in Robotics and AI (2026-09-10). https://doi.org/10.3389/frobt.2026.1901093

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.