Scholarlink

This study examined the relationship between artificial intelligence (AI) literacy and dependency and adjustment to higher education among students with and without learning disabilities (LD). Additionally, it investigated how these factors predict adjustment to higher education. Dependency on AI refers to the need to rely on automated systems for decisions, tasks, or validation. Advanced technologies like AI have the potential to simplify processes and enhance efficiency, but they can also instill a sense of insecurity or fear of being left behind if one does not keep pace with them. Adjustment to higher education is a multifaceted concept encompassing academic, social, personal emotional, and institutional aspects, aimed to examine differences between students with and without LD and those suspected of having LD in terms of AI literacy and dependency. The research included 683 students: 349 without LD, 185 diagnosed with LD, and 149 who suspect they have an undiagnosed LD. Data collection utilized online questionnaires assessing AI dependency, AI literacy, and adjustment to higher education. Findings revealed that students suspecting undiagnosed LD showed higher AI dependency compared to neurotypical students, while no differences were found in AI literacy between groups. Notably, higher AI dependency predicted lower adjustment to higher education among neurotypical students and those with suspected LD, but not among diagnosed LD students. Higher literacy in using AI predicted better adjustment among neurotypical and diagnosed LD students, but not among those with suspected LD. These findings emphasize the importance of developing policies for integrating AI in higher education. Institutions should focus on fostering balanced AI literacy while preventing excessive dependency, particularly considering the varying needs of different student populations. The results suggest the need for targeted support and guidance in AI usage across different student groups, specifically students who are at risk to lower educational outcomes.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI Dependency and Adjustment to Higher Education: A Comparative Study of Students with Diagnosed, Suspected, and No Learning Disabilities

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Miriam Sarid,
  • Rony Tutian,
  • Hilit Maizel,
  • Maya Kalman-Halevi

摘要

This study examined the relationship between artificial intelligence (AI) literacy and dependency and adjustment to higher education among students with and without learning disabilities (LD). Additionally, it investigated how these factors predict adjustment to higher education. Dependency on AI refers to the need to rely on automated systems for decisions, tasks, or validation. Advanced technologies like AI have the potential to simplify processes and enhance efficiency, but they can also instill a sense of insecurity or fear of being left behind if one does not keep pace with them. Adjustment to higher education is a multifaceted concept encompassing academic, social, personal emotional, and institutional aspects, aimed to examine differences between students with and without LD and those suspected of having LD in terms of AI literacy and dependency. The research included 683 students: 349 without LD, 185 diagnosed with LD, and 149 who suspect they have an undiagnosed LD. Data collection utilized online questionnaires assessing AI dependency, AI literacy, and adjustment to higher education. Findings revealed that students suspecting undiagnosed LD showed higher AI dependency compared to neurotypical students, while no differences were found in AI literacy between groups. Notably, higher AI dependency predicted lower adjustment to higher education among neurotypical students and those with suspected LD, but not among diagnosed LD students. Higher literacy in using AI predicted better adjustment among neurotypical and diagnosed LD students, but not among those with suspected LD. These findings emphasize the importance of developing policies for integrating AI in higher education. Institutions should focus on fostering balanced AI literacy while preventing excessive dependency, particularly considering the varying needs of different student populations. The results suggest the need for targeted support and guidance in AI usage across different student groups, specifically students who are at risk to lower educational outcomes.