Jul 2026· Qriset Indonesia Journal of Community Service· Vol 1, pp. 73-83· 0 citations
TL;DR
Describing patterns in the influence of AI-based learning on the learning motivation of students enrolled in Computer Technology programs at universities in West Java, Indonesia indicated that AI-based learning supported motivation through efficiency, accessibility, and perceived competence, but unguided dependence could weaken independent reasoning.
Abstract
Artificial intelligence has become a common learning aid in higher education, yet its contribution to motivation may coexist with risks to students’ cognitive autonomy. This study examined descriptive patterns in the influence of AI-based learning on the learning motivation of students enrolled in Computer Technology programs at universities in West Java, Indonesia. A descriptive quantitative design was applied to questionnaire responses from 30 students selected through simple random sampling as reported in the original study. The instrument contained six Likert-scale statements covering personalized learning, feedback speed, access to learning resources, critical thinking, independent creativity, and self-confidence. The analysis combined agree and strongly agree responses and converted them into percentages. The strongest positive response concerned easier access to and summarization of learning resources (90%), followed by rapid feedback (87%) and personalized understanding (83%). Moderate risk patterns also appeared, including reduced critical thinking (63%), lower confidence because of repeated AI checking (60%), and reduced independent creativity (57%). The findings indicated that AI-based learning supported motivation through efficiency, accessibility, and perceived competence, but unguided dependence could weaken independent reasoning. Lecturers should design process-oriented assessments, require verification of AI output, and maintain direct academic interaction. Because the study used a small sample and descriptive analysis, the results should be treated as preliminary rather than causal.
Artificial Intelligence (AI) has emerged as an important technological advancement in modern education, transforming the way students obtain information, understand academic concepts, and engage in learning activities. AI-based learning applications, including AI chatbots, personalized learning platforms, intelligent tutoring systems, automated writing assistants, and other educational tools, are increasingly being adopted by college students for academic purposes. This study aims to examine students’ perceptions and adoption of AI-based learning applications and to identify the major factors influencing their willingness to use these technologies in their studies. The study focuses on students’ level of awareness, frequency of usage, perceived usefulness, ease of use, trust, learning effectiveness, and challenges associated with AI-based educational applications. A descriptive research design may be adopted to achieve the objectives of the study. Primary data can be collected from college students through a structured questionnaire, while secondary information can be obtained from journals, books, research reports, and reliable websites. Convenience sampling may be used for selecting respondents. The collected data can be analyzed using percentage analysis and the Chi-square test. The study is expected to provide useful insights into students’ acceptance, attitudes, and experiences regarding AI-based learning applications and highlight the potential role of AI in making higher education more effective, flexible, accessible, and student-centered.
Srividhya Km, Magesh G· International Journal For Mu...· 0 citations
This study examined the extent of artificial intelligence (AI) utilization and its relationship with the learning performance of junior high school students in the Schools Division of Tacloban City during School Year 2025–2026. It specifically determined the AI platforms used by learners, their frequency and duration of AI use, common academic tasks, AI competence, perceived challenges, and learning performance. It tested the relationship between AI utilization and learning performance. The study used a descriptive-correlational research design with 208 Grade 7 to Grade 10 students selected through stratified random sampling across school size categories. Data were gathered using a validated researcher-developed questionnaire and analyzed using descriptive statistics and Spearman’s rho at the 0.05 and 0.01 levels of significance. Findings showed that ChatGPT was the most commonly used AI platform. Overall AI utilization was moderate, with learners using AI mainly for academic clarification, lesson summarization, and improvement of written work. The learners demonstrated a generally competent level of AI competence, particularly in AI literacy and verification skills, but showed areas for improvement in technical skills and ethical citation practices. They also reported challenges related to misinformation, privacy, and possible overdependence on AI. They rated learning performance in engagement, motivation, productivity, and critical thinking as very satisfactory. AI utilization had significant relationships with selected aspects of learning performance, particularly motivation, task completion, and critical thinking, but showed no significant relationship with overall learning performance. These findings suggest that AI use may be associated with specific learning outcomes but should not be viewed as a direct predictor of overall learning performance. Based on these findings, the study proposed an AI-supported teaching enhancement strategy to promote responsible and effective AI use in learning.
Mark Elijah G. Pacanza, Lilia P. Adrales, V. Ariza· International journal of res...· 0 citations
Artificial Intelligence (AI) has become an increasingly important component of higher education, influencing teaching, learning, academic support, and institutional practices. AI-powered tools such as adaptive learning systems, intelligent tutoring systems, virtual assistants, and learning analytics are reshaping students’ academic experiences by improving access to information, supporting self-paced learning, and enhancing engagement. At the same time, the growing use of AI in higher education has raised concerns relating to privacy, ethical use, digital fatigue, and the quality of human interaction in learning environments. Against this background, the present study examines undergraduate students’ perceptions of AI tools and their influence on student well-being and daily academic work in degree colleges of Rajouri District, Jammu and Kashmir.
The study adopted a quantitative approach using a descriptive survey design. Primary data were collected from 387 undergraduate students selected through stratified random sampling from degree colleges in Rajouri District. A structured questionnaire consisting of 25 statements measured on a five-point Likert scale was used to assess five dimensions: mental and emotional well-being, social interaction, academic engagement and performance, ethical concerns and privacy, and inclusivity and accessibility. Data were coded in Microsoft Excel and analyzed in SPSS Version 26 using descriptive statistics such as minimum, maximum, mean, and standard deviation.
The findings reveal that students generally perceive AI as beneficial for reducing academic stress, improving confidence, supporting workload management, and enhancing learning efficiency. Academic engagement and inclusivity emerged as especially strong dimensions, with students reporting that AI tools save time, support self-paced learning, and should be made equally accessible to all learners. However, the study also found that AI tools are not perceived as significantly improving peer interaction, teamwork, or collaborative learning, and many students expressed concern about privacy, ethical use, and mental fatigue associated with prolonged AI use. The paper concludes that AI has considerable potential to enrich higher education, but its educational value depends on balanced, ethical, and inclusive implementation supported by institutional transparency and human guidance.
M. Devi, Darakhshan Anjum· International Journal For Mu...· 0 citations
Artificial intelligence (AI), especially generative AI tools such as ChatGPT, has rapidly transformed modern education and students' learning behaviors. While AI provides students with convenient access to information, personalized feedback, and flexible learning support, its influence on students' autonomous learning in higher education remains controversial. Drawing on Self-Regulated Learning and Self-Determination Theory, this paper presents a theoretical analysis of the dual effects of AI on students' autonomous learning. Existing literature suggests that AI can strengthen self-directed learning, improve learning efficiency, and increase access to educational resources. However, excessive dependence on AI may weaken critical thinking, reduce independent problem-solving, and diminish meaningful interaction between students and teachers. This paper argues that AI itself is neither entirely beneficial nor harmful. Instead, its impact largely depends on how students use AI and how educators integrate it into learning environments. When AI supports rather than replaces thinking, it can enhance autonomous learning. Otherwise, it may encourage passive dependence. The paper also suggests ways to use AI responsibly, so that it supports learning without taking it over.
Yuntao Liu· Lecture Notes in Education P...· 0 citations
The application of generative artificial intelligence (GenAI) in higher education has expanded rapidly, exerting a significant influence on multiple core dimensions of university students' learning. Based on the core framework of Self-Determination Theory (SDT), this study systematically reviews existing literature on the impact of GenAI on students' learning motivation. The findings suggest that GenAI can, to a certain extent, satisfy university students' basic psychological needs for autonomy and competence by providing personalized learning support, immediate feedback, and autonomous learning choices. It also promotes interaction among students and between students and learning content, thereby enhancing learning motivation. However, GenAI may also lead to excessive student dependence, the dissemination of inaccurate information, and a weakening of students' independent thinking abilities, thereby undermining their autonomy and perceived competence, and thus negatively affecting learning motivation. The study further highlights the need for scientific guidance and regulation regarding the use of GenAI in educational contexts.
Yu-Shi Zhu· Lecture Notes in Education P...· 0 citations
In recent year,Artificial Intelligence (AI) has become a crucial component in higher education, reshaping how students study, access information, and complete assignments.AI-powered tools, including chatbots, plagiarism detection software, and adaptive learning systems, enable more efficient, interactive, and personalised learning experiences. With increasing AI adoption in universities, understanding how these technologies affect students’ academic performance and learning behaviour is crucial. This study aims to examine the role of AI in enhancing academic performance among university students by focusing on perceived usefulness, ease of use, trust, risk, and learning engagement. Our findings indicate that Personalised Learning, Intelligent Feedback and AI-Driven Assessment & Learning Analytics significantly and positively affect university students academic performance. Among these, Personalised Learning and AI-Driven Assessment & Learning Analytics exerted the strongest influence on academic performance. These findings underscore the pivotal role of AI-driven tools in enhancing student behaviour and academic performance in higher education
D. Kee, Zhe-Tee Min, Tasnim Amani Abdul Razak et al.· Asian Pacific Journal of Man...· 0 citations
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