New Scale Reveals How Gifted Students Learn With Artificial Intelligence

Generative artificial intelligence has swept into classrooms faster than almost any educational technology before it, and psychologists are still scrambling to answer a deceptively simple question: how do students actually change the way they learn when a large language model is sitting beside them? A new study published in Current Psychology by Majed Saeed Aldalham of King Khalid University in Saudi Arabia offers one of the most rigorous answers to date. The research presents the development and validation of the Adaptive Learning Strategies in the Age of Artificial Intelligence Scale, or AI-ALSS, a 38-item instrument designed specifically for gifted upper-secondary students. Rather than treating AI as a mere study aid, the scale captures a deeper psychological shift, measuring how learners reorganize their agency, metacognition, creativity, and cognitive effort around tools that can generate text, solve problems, and answer questions on demand.

The motivation for the work lies in a genuine measurement gap. Classic instruments of self-regulated learning, such as the Motivated Strategies for Learning Questionnaire and the Learning and Study Strategies Inventory, were built for classrooms where the learner, the teacher, and the textbook were the only relevant actors. They were never designed to probe what happens when a student must decide how much to trust a chatbot, when to verify its output, or how to integrate machine-generated material with their own reasoning. Recent reviews of ChatGPT’s impact on education have documented both promising gains, including improved creative problem-solving performance in experimental settings, and worrying risks such as cognitive offloading, in which students outsource thinking they should be doing themselves. Without a validated measure of AI-integrated learning strategies, researchers and educators could not distinguish adaptive, sophisticated AI use from passive dependency.

Aldalham chose gifted upper-secondary students as the target population for both theoretical and practical reasons. Gifted education has increasingly shifted from a static conception of fixed ability toward talent development models that emphasize evolving complexity and creative productivity, frameworks associated with scholars such as Dai, Renzulli, and Subotnik and colleagues. Within these models, high-ability learners are expected to self-regulate intensely, pursue independent inquiry, and push beyond standard curricula, making them an ideal group in which to observe the most advanced forms of AI-integrated strategy. The study also aligns with Saudi Arabia’s National Strategy for Data and AI, which positions artificial intelligence capability as a national priority, giving the research a clear policy context alongside its psychological one.

Methodologically, the study followed an exploratory mixed-methods scale-development design, a sequence widely recommended in contemporary guidelines for constructing psychological measures. Item generation began with a literature-based definition of the construct, refined through expert input and then subjected to quantitative content evaluation using content validity index procedures rooted in the classical work of Lawshe and later refinements by Polit, Beck, and Owen. Only after the item pool survived this content scrutiny did the study move to psychometric testing, which was conducted with a sample of 504 gifted students. Crucially, the sample was randomly split into two halves, one for exploratory analyses and one for confirmatory analyses, a practice that guards against the common pitfall of discovering a factor structure and then validating it in the very same data.

The exploratory phase combined traditional exploratory factor analysis with exploratory graph analysis, a newer psychometric network technique developed by Golino and Epskamp that estimates the number of underlying dimensions by examining the connectivity structure among items. Both approaches converged on the same conclusion: AI-integrated adaptive learning is not one thing but four related things. The first dimension, Autonomous AI-Driven Learning Agency, reflects the degree to which students take initiative in directing their own learning with AI, setting goals and pursuing inquiry rather than waiting for instruction. The second, Human-AI Metacognitive Calibration, captures the delicate skill of monitoring and adjusting trust in AI outputs, knowing when to accept a machine’s answer and when to challenge it, a theme that echoes the adaptive trust calibration literature of Okamura and Yamada.

The third and fourth dimensions extend the construct into higher cognitive territory. Creative-Adaptive Knowledge Innovation measures the extent to which students use AI as a springboard for original thinking, transforming and recombining machine-generated content into something genuinely new rather than merely reproducing it. AI-Augmented Cognitive Orchestration, a term borrowed conceptually from Dillenbourg’s work on classroom orchestration, describes the strategic coordination of cognitive resources, in which the learner allocates tasks between their own mind and the AI system to manage cognitive load and maximize understanding. Together, these four dimensions sketch a portrait of the AI-era learner as an active conductor of a hybrid human-machine cognitive system, not a passive consumer of generated answers.

The confirmatory phase provided strong statistical support for this architecture. Confirmatory factor analyses endorsed both a correlated first-order model, in which the four dimensions relate laterally, and a higher-order model, in which they load onto a single general factor of AI-integrated adaptive learning. Reliability estimates, including modern internal-consistency coefficients such as omega alongside traditional measures, were satisfactory for all four subscales, and a two-week test-retest assessment demonstrated that scores are stable over time, a key requirement for any instrument intended to track students across semesters or interventions. Convergent and discriminant validity indices, evaluated with procedures such as the Fornell-Larcker criterion, fell within acceptable ranges, indicating that the scale measures what it claims and does not merely duplicate existing constructs.

Two additional findings strengthen the scale’s practical credentials. First, tests of measurement invariance showed configural, metric, and scalar invariance across gender, meaning the instrument functions comparably for boys and girls and that score differences between groups can be interpreted substantively rather than as measurement artifacts, following the sensitivity framework articulated by Chen. Second, AI-ALSS scores correlated positively with self-reported grade point average, offering preliminary concurrent criterion-related evidence that students who deploy more sophisticated AI-integrated strategies also tend to achieve more academically. While a self-reported correlation cannot establish causation, it suggests the scale taps something educationally meaningful rather than a stylistic tendency to answer questionnaires favorably.

The implications reach well beyond gifted education in Saudi Arabia. For researchers, the AI-ALSS provides a continuous measure that can finally connect the self-regulated learning tradition, from Zimmerman through Pintrich to contemporary work by Järvelä and Hadwin, to the emerging science of human-AI collaboration championed by Shneiderman’s human-centered AI framework. For teachers and school leaders, the four dimensions offer a diagnostic vocabulary: a student may show strong creative innovation with AI but weak metacognitive calibration, blindly trusting outputs, while another may orchestrate tools expertly yet rarely initiate independent inquiry. Such profiles could guide targeted coaching instead of blanket policies that either ban or uncritically embrace AI. For policymakers navigating the tension between AI literacy goals and fears of cognitive offloading, the scale offers a way to monitor whether integration strategies are actually adaptive.

The author is careful to frame the results as initial rather than definitive. The validation was conducted in a single cultural context with a specific population, and the study itself calls for future work on predictive validity, sensitivity to intervention-related change, and cross-cultural replication. Whether the four-dimension structure holds for mainstream students, for younger learners, or in classrooms with different levels of AI access remains an open empirical question. Yet the study arrives at a moment when such tools are urgently needed, as generative AI reshapes homework, assessment, and the very definition of studying. By transforming a vague intuition, that students learn differently with AI, into a measurable, multidimensional construct with documented reliability and validity, the research gives the field something it has lacked: a ruler for the age of artificial intelligence. How educators, psychologists, and students wield that ruler may shape how the next generation learns to think alongside machines.

Subject of Research: Development and validation of a scale measuring AI-integrated adaptive learning strategies in gifted students

Article Title: Adaptive learning strategies in the age of artificial intelligence: development and validation of a scale for gifted students

Article References: Aldalham, M. S. (2026). Adaptive learning strategies in the age of artificial intelligence: development and validation of a scale for gifted students. Current Psychology, 45(20), Article 1606. https://doi.org/10.1007/s12144-026-10137-1

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10137-1

Keywords: artificial intelligence, adaptive learning, gifted students, scale development, self-regulated learning, metacognition, generative AI, psychometrics, education, Saudi Arabia, cognitive offloading, measurement invariance