Is generative artificial intelligence (GenAI) undermining how students think about their own learning? Rather than doing metacognitive tasks themselves, some students are delegating such tasks to AI systems. In other words, they are using GenAI to set their educational goals, check whether they understand something, decide if they need to study more, choose the best way to solve a problem, and evaluate their progress.
Artificial Intelligence (AI) vs. Generative Artificial Intelligence (GenAI)
Artificial intelligence or AI is the broad field—it includes all types of AI—it is the umbrella term. Generative AI or GenAI is a type of AI that creates new content.
AI systems that drive cars, translate from one language to another, recognize faces, detect fraud, or diagnose diseases do not create anything. These AI systems mainly analyze, predict, classify, identify patterns, detect anomalies, recommend actions, or make decisions.
GenAI creates new things, such as:
- Text
- Images
- Music
- Computer code
- Videos
- Voices and avatars
Metacognitive Laziness
Students are increasingly using GenAI tools, especially in academic settings. Professors, teachers, researchers, parents, and education experts are becoming concerned about how students are using GenAI tools and whether their growing usage hinders their own learning.
Of particular concern is many students’ tendency to get their AI systems to carry out metacognitive tasks, such as comprehension monitoring, goal setting, and self-evaluation, rather than carrying them out themselves. A new term has emerged—metacognitive laziness—which refers to this very phenomenon.
A team of researchers from The Education University of Hong Kong, The University of Hong Kong, and Monash University in Australia set about measuring this tendency.
John Ian Wilzon T. Dizon, a PhD student at the University of Hong Kong, and colleagues wrote about their study in the peer-reviewed journal ECNU Review of Education (citation below).
Metacognitive Laziness Scale (MLS)
The team created a Metacognitive Laziness Scale (MLS)—a six-question survey to measure the extent to which students rely on GenAI to avoid managing or thinking about some aspects of their own learning.
The authors wrote:
“The introduction of GenAI into education represents a double-edged sword. While it offers unprecedented efficiencies in task completion, it may simultaneously undermine the metacognitive processes that are foundational to deep learning and academic achievement.”
The MLS questions were adapted from an existing survey which measures people’s tendency to avoid work. They adapted them to focus specifically on students’ use of GenAI. Here is an example of how they adapted some of the questions:
Original item: “I choose easy options in school so that I don’t have to work too hard.”
Reworded item: “I choose to use AI for assignments, so I don’t have to think too hard.”
Before gathering the data, two content experts checked and further adapted the material for relevance and clarity; they did so independently.
The researchers administered the scale to 144 volunteers. They were university students who were studying health-related degrees across six disciplines at a Hong Kong university. The students completed the scale after attending a three-week interprofessional education simulation course.
The sample covered a very small geographical area and was modest in size, which constrains the generalizability of their findings, the authors acknowledged.
Promising Results
The authors said that their study’s initial results were promising, suggesting that their 6-question scale could be a reliable way to measure students’ metacognitive laziness. They also found a link between metacognitive laziness and more negative attitudes and behavior towards learning. They added, however, that no meaningful link was found with student engagement.
The authors wrote:
“We hope that our small effort to contribute to advancing our understanding of AI-driven metacognitive laziness will ignite traction among the community of practitioners to inform the enrichment of their research agenda.”
The researchers stressed that it is too early to draw firm conclusions; their findings are preliminary and it is not yet possible to establish cause and effect. In other words, it is not yet clear whether relying on GenAI in this way reduces students’ ability to set educational goals, decide whether they need to study more, check comprehension, evaluate their progress, and choose the best way to solve a problem. Neither is it possible to conclude that students who already have a tendency to avoid mental effort are simply more likely to rely on GenAI.
Further studies are required, they explained—studies involving larger and more diverse groups of students.
The Metacognitive Laziness Scale (MLS) is available for people who want to study how students rely on GenAI to help them manage their own learning. The authors hope the scale will help researchers and educators better understand when GenAI supports or gets in the way of learning. They also hope that it will be adapted in different educational setting.
Citation
Dizon, J. I. W. T., Mendoza, N. B., Gasevic, D., & Ganotice, F. A. Jr. (2026). Assessing AI-Driven Metacognitive Offloading: Initial Development and Validation of the Metacognitive Laziness Scale. ECNU Review of Education, 9(2). https://doi.org/10.1177/20965311261450994
