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IJIE (Indonesian Journal of Informatics Education)
Published by Universitas Sebelas Maret
ISSN : -     EISSN : 25490389     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering
IJIE (Indonesian Journal of Informatics Education) is is a scientific journal promoting the study of, and interest in, informatics education. The journal publishes empirical papers on information systems, informatics, the use of technology learning, and distance learning. It is an international journal published by the Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret, Indonesia bi-annually on June and December (ISSN: 2549-0389 (Online))
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Articles 107 Documents
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Exploring AI Literacy Levels Among University Students Through the Lens of Self-Efficacy: A Case Study From a Historically Disadvantaged University Vusumzi Funda
IJIE (Indonesian Journal of Informatics Education) Vol 9, No 2 (2025): (IJIE) Indonesian Journal of Informatics Education - December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v9i2.108138

Abstract

Artificial intelligence (AI) is increasingly embedded in higher education, yet the role of psychological factors such as self-efficacy in shaping students’ readiness to use AI remains underexplored. This study examines how self-efficacy influences AI literacy levels, defined as students’ ability to understand, apply, and critically evaluate AI tools, among university students at a historically disadvantaged South African institution. Guided by Bandura’s self-efficacy theory, a quantitative survey was conducted with 153 students using a structured questionnaire measuring mastery experiences, vicarious experiences, social persuasion, and emotional states. Results show that more than 70% of students expressed confidence in understanding AI concepts, while 78% reported being able to learn new AI tools with ease. Patterns in the descriptive statistics further suggest that students who reported prior experience with AI, encouragement from peers and lecturers, and low levels of anxiety tended to express higher confidence in using AI. These findings indicate that self-efficacy is a critical enabler of AI literacy, with psychological readiness complementing technical competence. The unique contribution of this study lies in its focus on students from a resource-constrained, historically disadvantaged university and its integration of Bandura’s four self-efficacy dimensions into the study of AI literacy. The results suggest that interventions such as peer mentoring programs, hands-on AI workshops, and structured feedback sessions can enhance both confidence and competence, thereby supporting equitable AI adoption in higher education..
Integrating AI in STEM Education in Africa: A Systematic Review of Best Practices and Perspectives Rogerant Tshibangu; Nokukhanya Thembane
IJIE (Indonesian Journal of Informatics Education) Vol 9, No 1 (2025): (IJIE) Indonesian Journal of Informatics Education - July
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v9i1.98421

Abstract

The integration of Artificial Intelligence (AI) in higher education is gaining global momentum, particularly within STEM (Science, Technology, Engineering, and Mathematics) disciplines. In Africa, however, the adoption of AI in STEM education remains fragmented and underexplored. This systematic review offers a novel and comprehensive synthesis of 46 peer-reviewed studies to assess how AI is being integrated into STEM education across African higher education institutions. Using the PRISMA framework, the review applies both thematic analysis and a PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) lens to discover patterns, regional disparities, and systemic barriers. The study reveals three key innovations: first, it integrates a wide range of theoretical perspectives including Diffusion of Innovations, Constructivist Learning Theory, Cognitive Load Theory, and Postcolonial Theory to interpret the socio-technical and pedagogical dynamics of AI adoption. Second, it develops a strategic, context-sensitive framework to guide the equitable and sustainable implementation of AI in STEM education, aligned with the UN Sustainable Development Goals (SDGs 4 and 9). Third, it critiques Eurocentric approaches to AI adoption and calls for a decolonized, locally adaptive model of AI integration. Major findings include infrastructure deficits, insufficient lecturer training, ethical and policy gaps, and the digital divide all of which hinder AI’s transformative potential in African STEM education. Yet, the increasing use of AI tools like ChatGPT, Intelligent Tutoring Systems, and LMS platforms post-2020 signals a turning point. This review advances a nuanced, Africa-centered roadmap for AI in STEM education and contributes original theoretical and strategic insights to the global discourse on educational innovation.
The Role of Artificial Intelligence in Enhancing Critical Thinking in Education : A Systematic Literature Review Rahmat Alvin Tarwanto; Yudianto Sujana; Nurcahya Pradana Taufik Prakisya
IJIE (Indonesian Journal of Informatics Education) Vol 9, No 2 (2025): (IJIE) Indonesian Journal of Informatics Education - December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v9i2.103868

Abstract

The emergence of generative AI, particularly language models like ChatGPT, has revolutionized educational practices by enhancing lesson planning and fostering critical thinking. This systematic literature review investigates the application of generative AI in creating effective lesson plans and its broader role in improving the educational process. By synthesizing findings from multiple studies, this research highlights AI's ability to personalize learning, provide adaptive feedback, and simulate real-world scenarios, which collectively promote analytical and reflective thinking among students. Additionally, the integration of ethical considerations in AI-supported education fosters responsible use and critical evaluation of AI systems. Despite its potential, challenges such as ethical dilemmas, dependency on technology, and algorithmic biases remain significant. This study underscores the transformative role of generative AI in modern education, offering practical insights and recommendations for integrating AI tools effectively. The findings contribute to understanding AI's impact on pedagogy, student engagement, and the development of higher-order thinking skills, emphasizing the importance of a balanced approach that aligns AI capabilities with human.
Balancing Ethics and Privacy in the Use of Artificial Intelligence in Institutions of Higher Learning: A Framework for Responsive AI Systems Belinda Ndlovu; Kudakwashe Maguraushe
IJIE (Indonesian Journal of Informatics Education) Vol 9, No 1 (2025): (IJIE) Indonesian Journal of Informatics Education - July
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v9i1.100723

Abstract

Artificial Intelligence (AI) is swiftly revolutionising higher education, presenting substantial prospects for innovation while concurrently evoking ethical and privacy concerns. These concerns include issues such as intrusive data collection, algorithmic bias, threats to student autonomy, and unequal access to AI-enhanced learning. Without clear guidelines or institutional safeguards, there is a risk that AI systems may reinforce existing social inequalities, compromise student privacy, and erode the human-centred nature of teaching and learning. This research presents an AI framework that is responsive and ethical within the area of higher education. The process involved sixteen in-depth interviews from university students, administrators, lecturers, and IT professionals belonging to three separate universities, with the Technology Organisation Environment model and the sociocultural learning theory being employed. Thematic analysis identified ten critical themes centred around benefits, challenges, applications, responsible use, privacy and data security, ethical considerations, institutional policies and frameworks, training, equity, and sustainable AI use. The results indicate that institutions should take a proactive stance in dealing with these issues and harnessing AI's full potential. The study thus advances the formulation of policies that provide for the equitable distribution of AI technologies, with the accompanying strong emphasis on ethical principles and data security. In preserving academic integrity and enhancing educational processes, this research stresses the need to create collaborative environments among the major stakeholders. Thus, higher education, taking into consideration AI implementations aligned with learner, educator, and administrator interests, thereby offers the promise of navigating the complex terrain of AI integration.
Real-Time Emotion Recognition in Online Learning Using Google Teachable Nazli Rahmeisi
IJIE (Indonesian Journal of Informatics Education) Vol 9, No 2 (2025): (IJIE) Indonesian Journal of Informatics Education - December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v9i2.110565

Abstract

Understanding learners’ emotional engagement in e-learning environments remains challenging due to the limited availability of non-verbal cues, despite its importance for motivation and participation. This paper proposes a facial emotion recognition approach using Google's Teachable Machine to support real-time emotion detection within online learning environments. The system analyzes facial expressions captured through a standard webcam to classify four basic emotional states: happy, sad, neutral, and angry. An experimental design was employed using simulated emotional expressions collected under controlled conditions, including adequate lighting and front-facing facial images. The results indicate that the system can provide instructors with additional affective cues to support formative assessment and instructional awareness in synchronous online learning. The proposed approach emphasizes practical instructional feasibility and accessibility compared to more complex emotion recognition models, as it does not require specialized hardware or advanced programming skills.
The Role of Artificial Intelligence in Programming Education and Its Impact on the Learning Process Bagas Dwiantoro; Yudianto Sujana; Puspanda Hatta
IJIE (Indonesian Journal of Informatics Education) Vol 9, No 1 (2025): (IJIE) Indonesian Journal of Informatics Education - July
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v9i1.103884

Abstract

The integration of Artificial Intelligence (AI) into programming education has rapidly expanded, offering both promising opportunities and complex challenges. This study conducts a systematic literature review (SLR) using the PRISMA framework to examine the influence of AI tools on creativity, collaboration, and technology acceptance in programming education. A total of 33 peer-reviewed studies published between 2022 and 2024 were analysed to explore the pedagogical impact of AI. The findings indicate that AI tools support creative problem-solving, enhance collaborative learning, and increase student engagement through personalized feedback and adaptive learning environments. Despite these benefits, concerns remain about the potential for over-reliance on AI, reduced critical thinking, and ethical issues such as bias and authorship. While AI encourages iterative and imaginative approaches to programming, its successful implementation depends on instructional strategies that promote reflection, responsible tool use, and alignment with real-world programming practices. This study emphasizes the importance of balancing the advantages of AI with thoughtful pedagogy to support meaningful learning. Future research is recommended to investigate the long-term effects of AI on student development and to refine frameworks for integrating AI tools into programming education.
The Effect of Applying Scaffolding Method on Students' Programming Abilities at Surakarta State Vocational School Steven Budi Sanjaya; Yusfia Hafid Aristyagama; Dwi Maryono
IJIE (Indonesian Journal of Informatics Education) Vol 9, No 2 (2025): (IJIE) Indonesian Journal of Informatics Education - December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v9i2.87704

Abstract

This research aims to determine the effect of applying the scaffolding method on the programming abilities of students at Surakarta Vocational School as measured by tests or programming ability tests. By using the scaffolding method, it is hoped that students' programming abilities will be better than conventional learning methods. The research used a quasi-experimental design with a pre-test and post-test control group design. In the research, the control class was not given treatment in learning activities, while the experimental class was given treatment in the form of a scaffolding method in learning activities. The research subjects were 72 class X students at a Vocational School in Surakarta obtained by cluster random sampling. Research data was obtained using pre-test and post-test which were prepared based on basic programming concepts using the Java programming language. Research data analysis was carried out using descriptive analysis techniques using the SPSS application. The results of the research show that the application of the scaffolding method has a good effect on the programming abilities of students at Surakarta Vocational School. This can be seen from the average programming ability test result for the experimental class which is higher than the control class (79.58 > 74.72). Additionally, the gain score analysis revealed that the improvement in the experimental class (49.72%) was significantly higher than the control class (41.94%). It can be concluded that the programming ability of the experimental class using the scaffolding method is better than the control class.

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