The quantitative half of the study relied on a structured questionnaire administered online over fourteen weeks, covering demographics, patterns of AI use, and four psychological constructs: perceived learning outcomes, motivation and engagement, self-regulated learning, and digital literacy. Respondents rated their agreement with statements on five-point Likert scales, and the researchers computed composite scores for each construct before running descriptive statistics, Pearson correlations, independent-samples t-tests, and one-way analyses of variance. The sample had a mean age of 16.59 years, was 60.7 percent female, and 83.3 percent reported regular home internet access. Reliability was strong across most scales, with Cronbach’s alpha values exceeding 0.80 for the AI usage, learning outcomes, motivation, and self-regulation measures.

The usage data paint a picture of AI as an overwhelmingly mobile, chatbot-centered phenomenon. Some 74 percent of students accessed AI tools primarily through their smartphones, with tablets and laptops trailing far behind and not a single student reporting use of school computers. Chatbots dominated the tool landscape, with 80 percent of respondents reporting their use, followed by math problem-solving applications at 26 percent, writing assistants at 16.7 percent, and adaptive learning platforms at just 7.3 percent. The most common academic task was homework assistance, with a mean frequency rating of 3.31 on the five-point scale, while tools recommended by teachers scored lowest, suggesting that students are discovering and deploying AI largely on their own initiative rather than through institutional channels.

Perceptions of learning benefits were broadly positive but measured. A majority of students, 65.4 percent, agreed that AI improves their understanding of the subjects they study, roughly half said it provides clearer explanations than their textbooks and helps them correct mistakes, and about 48 percent credited AI with better grades or deeper skills such as critical thinking. Notably, only 32 percent admitted to depending on AI to the point of no longer attempting problems independently, a figure that hints at the tension running through the entire study. On the motivational side, 51.3 percent said AI increased their interest in certain subjects, 53.3 percent found homework less stressful with AI, and 42 percent reported greater classroom confidence after using the tools.

The correlation analysis revealed a tightly interwoven network of positive associations. Perceived learning outcomes correlated strongly with motivation and engagement, with a Pearson coefficient of 0.739, and moderately with self-regulation and digital literacy. Self-regulation and digital literacy were themselves strongly linked at 0.670. All associations were statistically significant at the 0.01 level. Group comparisons added nuance: no significant gender differences emerged on any scale, with effect sizes ranging from negligible to small, but educational level mattered for learning outcomes and motivation, where one-way ANOVAs produced significant effects across the three school levels studied, though post-hoc Tukey tests showed the pairwise differences were concentrated between specific grade cohorts.

The qualitative phase, involving semi-structured online interviews with twelve purposefully selected students, uncovered the study’s most striking finding: what the researchers call the laziness paradox. Half of the interviewed students reported that AI boosted their motivation by making difficult tasks feel manageable and building confidence before tests, while an equal half described the opposite effect, a slide into cognitive passivity when ready-made answers removed the need for independent effort. One respondent admitted, honestly, to feeling lazy when using AI because a complete answer arrives without struggle, while another warned that AI makes students lazy and erodes self-confidence. A third of the interviewees occupied an ambivalent middle ground, reporting that the motivational outcome depended entirely on their own self-regulatory discipline in any given moment.

Perhaps most surprising is how skeptical these teenagers are. Trust in AI outputs was overwhelmingly conditional: half of the interview cohort practiced what the researchers describe as triangulation, cross-checking AI responses against textbooks, official course notes, and teacher explanations before accepting them. Others compared outputs across competing AI platforms or consulted peers and instructors. This verification behavior suggests that traditional academic authorities retain epistemic primacy even in an AI-saturated study environment, and it represents an emergent form of AI literacy that goes beyond mere technical competence. Yet the skepticism had clear subject boundaries. Students trusted AI for language tasks such as grammar correction and rephrasing but expressed deep mistrust in mathematics and physics, where they reported hallucinated answers, methods that diverged from classroom instruction, and explanations pitched at a university level rather than their own developmental stage.

Ethical reasoning also featured prominently in the interviews. A majority of students, 58 percent, drew a firm internal line between using AI as a cognitive scaffold to understand lessons and using it to complete assignments dishonestly, and several expressed anxiety about being misjudged by teachers, wishing their instructors understood that they use AI to learn rather than to cheat. Teachers themselves emerged as powerful regulators of student behavior: 58 percent of respondents said their educators actively addressed AI in class, mostly steering students toward responsible, moderate use rather than absolute prohibition, though a minority of students perceived their teachers as simply hostile to the technology. The researchers argue that this pedagogical framing matters enormously, because students’ ethical frameworks appear to strengthen when teachers shift from prohibition toward critical, mediated guidance.

The study’s authors frame their results through constructivist learning theory and the Technology Acceptance Model, noting that perceived ease of use functions as a double-edged sword. When an AI tool is frictionless, learners can redirect cognitive bandwidth toward higher-order reflection and knowledge construction, but the same frictionlessness enables bypassing productive cognitive struggle altogether, shifting motivation from mastery-oriented learning toward mere task completion. Half of the interviewed students showed meta-cognitive awareness of this danger, with one observing that information retrieved easily is forgotten just as easily because it is never properly processed. The researchers also documented structural barriers beyond psychology: multimodal failures when students uploaded dense photographed documents, and a fundamental mismatch between the generic training corpora of mainstream models and the specific methods demanded by Moroccan school curricula.

The practical implications are pointed. Rather than restrictive bans, the authors advocate responsible integration: redesigning traditional homework into AI-supported critique activities in which students are evaluated on their ability to analyze, edit, and justify AI-generated content; teaching prompt engineering, source triangulation, and hallucination detection as explicit components of digital literacy curricula; and developing subject-specific prompting frameworks that acknowledge the different risk profiles of language versus STEM applications. Policymakers, they argue, should establish clear, context-sensitive guidelines and invest in teacher professional development rather than defaulting to punitive prohibition. The study’s limitations, a convenience sample from a single Moroccan region, self-reported perceptions rather than objective performance data, and a cross-sectional design that cannot establish causality, mean the findings are exploratory. But they deliver a clear message for educators worldwide: the educational value of generative AI is not inherent in the technology but contingent on pedagogical framing, learner agency, and the institutional norms that govern its daily use, and teenagers, left largely to their own devices, are already inventing the verification habits and ethical boundaries that schools have yet to formalize.

Subject of Research: Secondary school students' perceptions of AI-assisted learning, motivation, self-regulated learning, and digital literacy in Morocco

Article Title: Perceptions of AI-assisted learning and learner development among Moroccan secondary school students

Article References: Kandirou, R., El Youssfi, S., Elatlassi, M., Dahman, A., & El Hammoumi, M. M. (2026). Perceptions of AI-assisted learning and learner development among Moroccan secondary school students. Discover Education, 5(1), Article 1125. https://doi.org/10.1007/s44217-026-02109-1

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02109-1

Keywords: artificial intelligence in education, ChatGPT, secondary education, self-regulated learning, digital literacy, student motivation, generative AI, Morocco, mixed methods, academic integrity, AI hallucinations, perceived learning outcomes