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Anggit Pratiknyo Widyagiri
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ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: EXAMINING ITS IMPACT ON STUDENT LEARNING, ENGAGEMENT, AND ACADEMIC PERFORMANCE Anggit Pratiknyo Widyagiri
EduTech Journal Vol. 3 No. 2 (2026): JET - SEPTEMBER
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/4kf1y264

Abstract

Artificial Intelligence (AI) is increasingly integrated into higher education to support various learning activities, yet its overall impact on students remains inconsistently reported across the literature. This article reviews existing studies to examine the impact of AI on three interrelated aspects of higher education: student learning, student engagement, and academic performance. A literature review method was used, drawing on systematic reviews and meta-analyses that discuss AI applications and outcomes in higher education settings. The review shows that AI can support student learning through explanations, personalized feedback, and access to learning resources, while also raising concerns about overreliance and reduced critical thinking. Regarding engagement, evidence is relatively strong for behavioral engagement but more mixed for emotional and cognitive engagement. For academic performance, several systematic reviews and meta-analyses report positive effects of AI, particularly in STEM-related areas, although the specific impact of AI adoption still requires further investigation in certain learning contexts such as open and distance learning. Taken together, these findings suggest that the impact of AI in higher education should be examined not only through academic outcomes but also through how it shapes students' learning process and engagement, with AI functioning best as a supporting tool rather than a replacement for students' active involvement in learning.
The Effectiveness of Generative AI as a Learning Support Tool: Evidence from Higher Education in Indonesia Anggit Pratiknyo Widyagiri
EduTech Journal Vol. 3 No. 2 (2026): JET - SEPTEMBER
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/y60wh598

Abstract

Generative Artificial Intelligence (GenAI) has become an increasingly relevant learning support technology in higher education. This study examines its effectiveness in supporting learning among higher education students in Indonesia by synthesizing recent empirical and methodological evidence. A descriptive literature review was conducted using five relevant journal articles published between 2025 and 2026. The reviewed studies addressed perceived AI effectiveness in online learning, personalized learning, student engagement, digital and AI literacy, and machine learning-based analysis of learning effectiveness. The synthesis indicates that GenAI can support material comprehension, flexible individual learning, and personalized learning recommendations. One reviewed study reported an overall perceived effectiveness score of 3.44, with material comprehension reaching 3.55. Another reported a 15.03% increase in average academic performance among students using an AI-based learning analytics tool, compared with 4.63% among non-users. However, the evidence also shows that frequent AI use alone does not guarantee stronger engagement or learning outcomes. Digital and AI literacy, active student participation, lecturer guidance, institutional support, and responsible use influence the value of GenAI. Key risks include inaccurate information, cognitive dependency, reduced critical thinking, privacy and security concerns, infrastructure limitations, and academic integrity. Therefore, GenAI is best positioned as an augmentative learning support tool rather than a replacement for lecturers or independent learning. Future research should use longitudinal or experimental designs and objective learning indicators to establish stronger evidence of its effects.
DIGITAL LEARNING READINESS AND STUDENT ACADEMIC PERFORMANCE:THE MEDIATING ROLE OF LEARNING ENGAGEMENT Anggit Pratiknyo Widyagiri
EduTech Journal Vol. 3 No. 2 (2026): JET - SEPTEMBER
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/xn654g43

Abstract

The rapid expansion of digital learning environments has positioned digital learning readiness as a critical factor for student success, yet its influence on academic performance is often inconsistent across studies. This article reviews the literature to examine how learning engagement mediates the relationship between digital learning readiness and student academic performance, with particular attention to implications for mathematics education. A narrative-thematic literature review was conducted on articles retrieved from Scopus, ScienceDirect, ERIC, and PubMed Central, published between 2019 and 2026. The review indicates that digital learning readiness-encompassing students' technology-related knowledge, skills, and confidence for academic purposes-does not directly and consistently predict academic performance. Instead, its effect operates largely through learning engagement, defined as students' behavioral, cognitive, and emotional investment in academic activities. Empirical evidence shows that digital learning readiness significantly predicts learning engagement, which in turn is a strong and consistent predictor of academic achievement, whereas digital readiness alone shows a weaker direct path. Based on this synthesis, a conceptual framework and three research propositions are proposed to guide future empirical work. The article concludes that strengthening students' digital learning readiness must be accompanied by pedagogical strategies that actively cultivate learning engagement, particularly in technology-mediated mathematics instruction, and recommends further empirical validation of the proposed model within the Indonesian higher education context.
Personalized Learning Technologies and Student Achievement: Examining the Role of Digital Competence and Learning Motivation Anggit Pratiknyo Widyagiri
EduTech Journal Vol. 3 No. 2 (2026): JET - SEPTEMBER
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/yzqn3w98

Abstract

The development of personalized learning technologies, such as adaptive learning systems, intelligent tutoring systems, and artificial intelligence-based learning analytics platforms, has transformed the patterns of students’ interaction with learning materials, including in mathematics education. However, the effectiveness of these technologies in improving students’ learning achievement is unlikely to operate independently, but rather depends on students’ digital competence and learning motivation in utilizing such technologies. This article aims to examine, through a literature-based approach, the role of digital competence and learning motivation in the relationship between the use of personalized learning technologies and students’ learning achievement. The study employed a narrative-thematic literature review of selected articles retrieved from Scopus, ScienceDirect, ERIC, and PubMed Central databases and published between 2019 and 2025. The findings indicate that personalized learning technologies contribute positively to students’ learning achievement when supported by adequate digital competence, particularly in the dimensions of digital evaluation and self-management, as well as learning motivation rooted in the fulfillment of basic psychological needs for autonomy, competence, and relatedness, as explained by self-determination theory. Digital competence and learning motivation are positioned as variables that both moderate and mediate the effectiveness of personalized learning technologies in relation to students’ learning achievement. This article concludes that the integration of personalized learning technologies into mathematics education should be accompanied by efforts to strengthen students’ digital literacy and the design of learning environments that support intrinsic motivation. Further empirical research is recommended to examine the proposed conceptual framework within the context of mathematics education in Indonesia.
THE INTEGRATION OF ARTIFICIAL INTELLIGENCE IN EDUCATION: OPPORTUNITIES, CHALLENGES, AND IMPLICATIONS FOR DIGITAL LEARNING EQUITY Anggit Pratiknyo Widyagiri
EduTech Journal Vol. 3 No. 2 (2026): JET - SEPTEMBER
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/gtenvn51

Abstract

Artificial intelligence (AI) has moved from a long-standing research area in educational technology to a highly visible component of contemporary digital learning. This article reviews selected literature to examine the opportunities, challenges, and implications of AI integration for learning personalization, learning analytics, student experience, and digital learning equity. The review synthesizes a foundational conceptual paper, a systematic review, an experimental adaptive-learning study, a machine-learning forecasting study, and a 2025 student survey. The evidence indicates that AI can improve personalization, learning efficiency, formative feedback, and engagement when systems respond to both cognitive and affective conditions. An adaptive mathematics learning system, for example, produced better learning outcomes and lower mathematical anxiety when affective factors were considered alongside cognitive status. Learning analytics also demonstrated the potential to forecast computer-mediated communication competence from behavioral features. However, the literature highlights persistent concerns regarding accuracy, privacy, critical-thinking erosion, over-dependence, uneven model performance, and gaps between technical development and educational theory. The article argues that AI should therefore be integrated as an educational support mechanism rather than treated as a substitute for pedagogical judgment. From a research-team perspective, future work should connect mathematical and statistical modeling with educational theory, transparent evaluation, and equity-oriented governance.
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