ChatGPT for Operating Systems: Higher-Order Thinking in Focus
In this study, we propose an AI technology-based learning model using ChatGPT and investigate its effect on students’ higher-order thinking (HOT) ability in an operating systems (OS) course.
A critical requirement for IT and engineering students is supporting them in understanding advanced OS concepts and fostering their HOT. HOT involves analysis, evaluation, and creation of new understanding, which promotes deeper learning, improved outcomes, and enhanced student experience. An AI technology-based learning that supports OS instruction and focuses on HOT presents a solution. However, little is known about integrating such an emerging technology into pedagogical contexts, and further investigation is necessary to explore the best design and implementation and investigate its empirical effect. Pedagogical designs with appropriate theoretical foundations should be added to the literature on AI technology-based applications. In this study, we propose an AI technology-based learning model using ChatGPT and investigate its effect on students’ performance in handling HOT tasks. Constructivism theory suggests that knowledge is constructed through the individual interactive experiences of learners rather than being passively absorbed. The proposed learning model is inspired by constructivism, which grants students an active role in building their understanding during in-class instruction rather than passively receiving information from the instructor. ChatGPT activities are designed to provide learners with personalized and interactive instruction to build new knowledge about OS topics.
This study comprises 53 undergraduate students enrolled in an OS course offered by the College of Information Technology at AL al-Bayt University in Jordan. The participants were distributed into an experimental group that received AI technology-based instruction using ChatGPT activities and a control group that received traditional instruction. Students were randomly assigned to both groups, and both groups were taught by the same instructor during the academic year 2022–2023. The study was conducted with a quasi-experimental design using a HOT test to evaluate and compare the students’ responses to HOT OS problems.
This study is one of the few studies that investigates and measures the ability of ChatGPT to foster students’ HOT. This study offers a novel contribution to the literature by illustrating the importance of employing AI chatbots to improve students’ ability to handle HOT tasks. The effect reported in this study resulting from the use of ChatGPT provides empirical evidence and valuable insights for researchers, practitioners, and educators about the importance of using AI technology-based learning and its related issues. This can guide instructional designers to apply AI-based appropriately.
The findings show that the proposed AI technology-based learning model using ChatGPT improved students’ performance and supported their proficiency in responding to HOT OS tasks that span analyzing, evaluating, and creating cognitive levels. The proposed AI technology-based learning model that employed ChatGPT as a complementary tool helped students analyze, evaluate, and create a new self-understanding of OS topics, which fostered their HOT by 8.9%.
This study recommends that practitioners who utilize ChatGPT should combine other common in-class activities instead of replacing them. Thus, in-class activities become more attractive, increasing their students’ motivation.
Researchers need to understand how to design and implement ChatGPT activities and the factors enhancing their positive impact on students and learning.
AI technology-based learning using ChatGPT can help students improve their learning outcomes and help their instructors deliver content, leading to more efficient and effective higher education. Appropriate design and use of ChatGPT activities within traditional learning activities can promote and en-courage the use of such a technology.
Additional research should be conducted to explore and measure motivation, engagement, and students’ in-class interaction due to the use of AI technology-based learning. Further experiments on other courses with different environmental variables are highly recommended.
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