# How the use of artificial intelligence harms college students’ ability to learn > Source: > Published: 2026-08-19 12:00:43+00:00 College students who copy answers from artificial intelligence tools without verifying the information experience declines in their ability to learn independently. These unreflective habits are associated with a weaker belief in students’ own capabilities and a reduced motivation to learn. The findings were published in * Scientific Reports*. Generative artificial intelligence programs can quickly write essays, generate computer code, and solve advanced mathematical equations. These tools have become common academic aids on college campuses across the globe. When students use these systems to seek explanations or brainstorm new ideas, the technology can expand their academic perspectives and improve their studying efficiency. But reliance on automated answers carries potential academic risks. Hui Zhao and Huijuan Gu, researchers at Zhoukou Normal University, designed a study to examine what happens when students use artificial intelligence thoughtlessly. The researchers define thoughtless use as a pattern in which users blindly adopt machine-generated text without critically evaluating, verifying, or deeply understanding the outputs. The researchers wanted to know how this specific habit affects self-directed learning. Self-directed learning is a core educational skill in higher education. It refers to a student’s capacity to independently set goals, apply study strategies, and monitor their own academic progress. Students with high self-directed learning abilities can manage their time and actively evaluate what they do and do not understand. To understand the relationship between technology habits and study skills, the researchers looked at two psychological factors. The first is self-efficacy, which is a person’s internal belief in their capacity to successfully complete tasks and overcome challenges. The second factor is learning motivation, which encompasses the psychological drives that push a student to initiate and persist in their coursework. The researchers based their investigation on the idea that a student’s environment, personal beliefs, and daily behaviors all shape one another in a continuous, reciprocal cycle. Zhao and Gu recruited 487 undergraduate students from four universities in Henan Province, China, to complete an online survey. The participants answered a series of questions assessing their study habits on a five-point scale. The respondents ranged from college freshmen to seniors and represented a variety of academic disciplines, including the humanities, social sciences, and natural sciences. The survey measured four distinct categories using established psychological scales. First, it assessed how often students engaged in thoughtless artificial intelligence use. Participants rated their agreement with statements such as “I copy learning tasks or problem statements into generative AI to seek answers or ideas” and “I feel that generative AI is more capable than I am in learning and problem-solving.” Second, the survey measured the students’ academic self-efficacy. This section asked participants to reflect on their confidence when facing difficult materials, using prompts like “I believe that I can independently master complex learning content.” [Add PsyPost to your preferred sources](https://www.google.com/preferences/source?q=psypost.org) Third, the researchers measured the students’ overall motivation to learn. This included questions about their curiosity, their desire to master new skills, and the satisfaction they gained from overcoming academic hurdles. Finally, the survey evaluated the students’ capacity for self-directed learning by asking how often they actively checked their own understanding of the material or created knowledge frameworks like mind maps. The researchers used a statistical technique called structural equation modeling to analyze the survey responses. This analytical method allows scientists to observe complex networks of relationships between multiple different variables at the same time, rather than just looking at two variables in isolation. The analysis revealed that the thoughtless use of artificial intelligence is strongly associated with lower levels of self-directed learning. Students who habitually relied on automated answers reported worse self-management skills and lower cognitive engagement in their coursework. The researchers identified a specific psychological pathway explaining this decline. The authors suggest that an unreflective reliance on technology deprives students of the opportunity to struggle through complex problems. Without the experience of overcoming academic challenges through their own hard work, students reported lower levels of self-efficacy. This lower confidence in their own abilities was in turn linked to a reduced motivation to learn. When individuals do not believe they can succeed on their own, their willingness to invest effort naturally declines. With less motivation and lower self-confidence, the students became less capable of managing their own educational progress, acting instead as dependent users of external tools. The researchers also conducted a multi-group analysis to see if these patterns differed between men and women. This statistical test allowed the authors to compare the strength of the psychological relationships across the two demographic groups. The analysis uncovered distinct gender differences in how thoughtless technology use relates to psychological well-being. For male students, unreflective use showed a stronger negative association with learning motivation. The researchers suggest that male students often view technology primarily as a tool for efficiency, meaning that immediate automated answers quickly replace their internal drive to engage deeply with the material. For female students, thoughtless use was associated with steeper declines in self-efficacy and self-directed learning. The researchers note that female students tend to adopt more reflective and evaluative study strategies in their traditional coursework. When they bypass these reflective processes by copying automated answers, they miss out on the mastery experiences that traditionally build their academic confidence. The study relies on cross-sectional data, meaning all the information was collected at a single point in time. Because the researchers did not track the students over a long period, the statistical results highlight relationships between behaviors and beliefs but cannot definitively prove cause and effect. It is possible that students who already have low self-efficacy are simply more likely to use artificial intelligence thoughtlessly. The demographic makeup of the sample also presents some limitations. The participants were drawn exclusively from universities in a single Chinese province, and nearly eighty percent of the respondents were female. Expanding future surveys to include a more balanced demographic distribution across different regions would help verify if these patterns hold true for other student populations. Additionally, the survey measured motivation as a single, unified concept. Future research could split this category into intrinsic motivation, such as natural curiosity, and extrinsic motivation, such as a desire for good grades. Separating these concepts would provide a more detailed picture of how automated tools influence a student’s inner drive to succeed. Future studies might also track students over an entire semester to see how their habits and self-confidence evolve as they face different types of academic challenges. The study, “[Thoughtless Use of Generative Artificial Intelligence and College Students’ Self-Directed Learning: A Multi-Group SEM Analysis of Gender Differences](https://www.nature.com/articles/s41598-026-54337-y),” was authored by Hui Zhao and Huijuan Gu.