Artificial Intelligence In Education Research Articles

Enhancing enterprise resource planning learning through generative AI teaching assistants: A motivation – opportunity – ability perspective

ABSTRACT The rapid integration of generative artificial intelligence (AI) in higher education has prompted a growing interest in its pedagogical implications. However, there is limited empirical research examining how these tools influence student engagement in complex and practice-oriented courses, particularly in enterprise resource planning (ERP) education. This study investigates the impact of generative AI teaching assistants (GAITAs) on students’ learning engagement in ERP courses using a motivation – opportunity – ability framework. The participants were 242 upper-level undergraduate students of ERP courses at a university in Central Taiwan. Results showed that teacher empowerment positively influenced autonomous usage motivation and negatively affected controlled usage motivation. This study found significant moderating effects of GAITAs’ information quality, digital literacy, and information processing ability on the motivation – engagement relationship. This study contributes to the AI in education literature by proving the effectiveness of GAITAs in ERP education and offering practical insights for AI-assisted learning in higher education.

Knowledge and perception of artificial lntelligence education among undergraduate healthcare students.

Artificial intelligence (AI) is gaining recognition for its ability to enhance patient outcomes in healthcare. Therefore, integrating AI into the undergraduate curriculum is essential to equip students with foundational knowledge before graduation. This study investigated the knowledge and perceptions of AI education among undergraduate healthcare students. This cross-sectional study targeted students from various health colleges in Saudi Arabia and was conducted from January 1, 2024, to April 30, 2024. Data were collected using a validated questionnaire comprising three sections: demographics, knowledge, and perceptions of AI. We utilized a convenience sampling technique to distribute the survey. Descriptive analysis was performed to assess the baseline characteristics, and a multivariable logistic regression model was used to identify the covariates that significantly influence knowledge scores. A total of 303 students participated in our study, including 142 pharmacy and 161 non-pharmacy students. Non-pharmacy students demonstrated significantly greater knowledge of AI than pharmacy students (p = 0.0084). Positive perceptions of AI were more prevalent than negative perceptions among all participants (p = 0.0062). Pharmacy and non-pharmacy students held significantly different knowledge of advanced AI skills (p = 0.0019) and the importance of AI applications (p = 0.0259). Additionally, there were significant differences in perceptions regarding the belief that AI-powered medical devices would help healthcare providers reduce medical errors (p = 0.0279). Healthcare students recognize the benefit of AI in enhancing patient outcomes but lack practical knowledge of its medical applications. They believe integrating AI education into their curriculum is essential for acquiring skills before graduation. Therefore, enhancing AI training may foster innovation, enhance patient outcomes, and align with the evolving demands of the healthcare sector.

Bringing Artificial Intelligence (AI) into Health Information Seeking Behavior: A Study of AI and Information Seeking Research

This paper explores the impact of artificial intelligence (AI) on information seeking behavior research and practice, including the need to scrutinize existing information seeking theory, challenge the understood behavioral norms, and consider redefining information literacy and information retrieval education. The historical examination spans from the 1950s to the present, with a specific focus on recent developments in health information seeking and the evaluation of medical information sources. Key to this exploration are ongoing debates in healthcare, ethics, and AI and information literacy education, which represent important dimensions of the impact of emerging technology on information-seeking behavior. The insights provided by this research can be useful for both researchers and practitioners, aiding them in navigating the evolving landscape shaped by AI technology.

Application of Artificial Intelligence in Higher Education An Evaluation of Opportunities and Challenges: A Prospective Study

The current study aims to reveal possible opportunities for applying artificial intelligence in higher education, and to identify the most important challenges that such application faces and the maximum benefits that can be acquired from employing artificial intelligence techniques in developing the educational process and the educational services in higher education institutions. This study followed the descriptive analytical approach in addressing the topic of applying artificial intelligence in higher education by reviewing the literature and previous studies that addressed this topic. The study concluded that it is possible to benefit from the application of artificial intelligence in higher education in the fields of teaching, learning, administration, research, and others, if the requirements of this application are dealt with effectively and the facilities that support the use of artificial intelligence techniques are provided. The study recommends confronting the challenges that weaken the effectiveness of the application of artificial intelligence in higher education. The most important challenges in this regard are: the lack of educational policies related to the application of artificial intelligence in education, the weakness of the infrastructure needed to supports the use of artificial intelligence techniques, the need to train academics and learners on how to deal with new applications, and the lack of due awareness of the importance of artificial intelligence applications and its role in developing higher education. KEY WORDS: artificial intelligence, higher education, opportunities, challenges, foreseeing the future

Bibliometrix 패키지를 활용한 인공지능 융합 과학교육의 연구동향 분석: 국제저명학술지를 중심으로

Objectives This study aims to analyze the trends of artificial intelligence (AI)-integrated science education research published in SCI and ESCI-indexed journals and to provide implications for the direction of AI-integrated science education research in South Korea. Methods For this purpose, research articles related to AI and science education were collected from the Web of Science (WoS) database. After a data preprocessing process, 55 studies were selected for analysis. A detailed bibliometric analysis was conducted using the bibliometrix package in R. Results The study found that AI-integrated science education research has surged globally and domestically since 2022. Key research topics include generative AI, machine learning, learning assessment, and teacher education. However, while domestic studies predominantly focus on practical research, they lack sufficient attention to AI ethics and data analysis, particularly in areas such as critical thinking. Conclusions To advance AI-integrated science education research in South Korea, it is necessary to strengthen data-driven research and AI ethics studies. Additionally, efforts should be made to enhance teacher training programs and improve AI education environments, ensuring a more systematic, practical, and ethical approach to AI-integrated science education.

Personalizing Student Major Selection through Artificial Neural Network Prediction Models

This research focuses on a well-known problem in secondary education that a student may have a hard time in choosing their study major to fit their interest, gifts, and long-term goals. Concentrating on the SAINTEK (science and technology) and SOSHUM (social sciences and humanities) streams of Indonesian high schools, the study will incorporate an Artificial neural network (ANN) to provide an insight into the student preferences based on multidimensional data through modeling and predicting purposes. A 44-item questionnaire was distributed to 205 students of SMA Negeri 1 Karanganom to gather the inputs that involved personal interests, parental influence, career perspective, and psychosocial characteristics. It was trained and validated with Stratified K-Fold Cross Validation that delivered good performance scores of average accuracy at 87% and precision value at 89- 91 recall and an F1-score of 90. In addition to the algorithmic validation, qualitative interviews of some of the students indicated that the prediction of the model corresponds to what these students perceive about their academic leanings. The obtained results argue that ANN systems can be used, not only as an error-free classifier but also as an educational decision-support system that can be used to augment student guidance with personally-tailored, data-driven advice. The proposed study locates ANN in the larger pedagogical mission of responsiveness and student-centered planning as such, the study also advances the new discourse of ethical and effective uses of artificial intelligence in education.

The Genie in a Bottle: potential and major risks of the use of Artificial Intelligence in Education

Artificial Intelligence (AI) is an emerging and transformative force in education, offering both significant potential and major risks. On one hand, AI can significantly improve the educational experience by enhancing the accuracy and speed of assessment and grading, personalizing learning experiences, and enabling students to learn at their own pace. However, the integration of AI in education brings with it numerous challenges. AI use in education can perpetuate and exacerbate existing biases, invade student privacy, and result in job loss for educators. There are also ethical concerns surrounding the use of AI and the possibility of AI systems making decisions about students without their knowledge or consent. Given these considerations, the use of AI in education can be seen as a double-edged sword and a strong challenge for educators, policymakers, and technology developers to work together to establish responsible AI practices. Given the little knowledge of this matter within the educational community in general and the panic and concern about the bad applications of the few AI systems that have been released to the public, it is proposed in this article to address these issues on an AI-friendly instead of IA-proof approach and specific educational applications are suggested for the different types of AI that are currently known.

AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.

The integration of artificial intelligence (AI) into clinical practice, particularly within radiology, nuclear medicine and radiation oncology, is transforming diagnostic and therapeutic processes. AI-driven tools, especially in deep learning and machine learning, have shown remarkable potential in enhancing image recognition, analysis and decision-making. This technological advancement allows for the automation of routine tasks, improved diagnostic accuracy, and the reduction of human error, leading to more efficient workflows. Moreover, the successful implementation of AI in healthcare requires comprehensive education and training for young clinicians, with a pressing need to incorporate AI into residency programmes, ensuring that future specialists are equipped with traditional skills and a deep understanding of AI technologies and their clinical applications. This includes knowledge of software, data analysis, imaging informatics and ethical considerations surrounding AI use in medicine. By fostering interdisciplinary integration and emphasising AI education, healthcare professionals can fully harness AI's potential to improve patient outcomes and advance the field of medical imaging and therapy. This review aims to evaluate how AI influences radiology, nuclear medicine and radiation oncology, while highlighting the necessity for specialised AI training in medical education to ensure its successful clinical integration.

Extended Reality and Artificial Intelligence in Education: A Systematic Review of Motivational Outcomes Using the ARCS Framework

Background: Emerging technologies such as Artificial Intelligence (AI) and Extended Reality (XR) are reshaping educational practices by offering new ways to engage learners. Despite their growing presence in classrooms, there remains limited consolidated evidence on how these tools affect student motivation. The ARCS model focusing on Attention, Relevance, Confidence, and Satisfaction provides a valuable structure for evaluating motivational impacts in technology-enhanced learning. Purpose: This study aims to systematically review and analyze existing research that applies the ARCS motivational model to educational interventions using AI and XR technologies. It focuses on understanding the extent to which these technologies influence student motivation across different learning environments and instructional contexts. Methods: A comprehensive literature review was conducted across major academic databases, including Scopus, Web of Science, ERIC, and PsycNet. Studies were selected based on predefined inclusion criteria: empirical design, application of the ARCS model, and the use of AI or XR in educational settings. A total of 32 studies were included and synthesized using meta-analytic techniques to assess effect sizes related to motivational outcomes. Findings: The analysis revealed that the integration of AI and XR technologies within ARCS-based instructional designs significantly improves student motivation. Virtual reality demonstrated the strongest effects, particularly in enhancing learners’ attention and satisfaction. Results also indicated that face-to-face modalities slightly outperformed virtual ones in maintaining motivational engagement. Conclusion: The findings support the use of the ARCS model as an effective framework for leveraging emerging technologies to enhance motivation in education. Properly designed interventions that incorporate AI and XR can foster deeper engagement and improve motivational outcomes across a variety of educational contexts.

Analyzing Higher Education Students’ Prompting Techniques and Their Impact on ChatGPT’s Performance: An Exploratory Study in Spanish

Generative artificial intelligence is reshaping how people interact with digital technologies, emphasizing the need to develop effective skills for engaging with it. In this context, prompt engineering has emerged as a critical skill for optimizing AI-generated outputs. However, research on how higher education students interact with these technologies remains limited, particularly in non-English-speaking contexts. This exploratory study examines how 102 higher education students in Chile formulated prompts in Spanish and how their techniques influenced the responses generated by ChatGPT (free version 3.5). A quantitative analysis was conducted to assess the relationship between prompt techniques and response quality. Two emergent prompt engineering strategies were identified: the Guide Contextualization Strategy and the Specific Purpose Strategy. The Guide Contextualization Strategy focused on providing explicit contextual information to guide ChatGPT’s responses, aligning with few-shot prompting, while the Specific Purpose Strategy emphasized defining the request’s purpose, aligning with structured objective formulation strategies. The regression analysis indicated that the Guide Contextualization Strategy had a greater impact on response quality, reinforcing the importance of contextual information in effective interactions with large language models. As an exploratory study, these findings provide preliminary evidence on prompt engineering strategies in Spanish, a relatively unexplored area in artificial intelligence education research. Based on these results, a methodological framework is proposed, encompassing four key dimensions: grammatical skills; prompt strategies; response from the large language model; and evaluation of response quality. This framework lays the groundwork for future artificial intelligence digital literacy interventions, fostering critical and effective engagement with generative artificial intelligence while also highlighting the need for further research to validate and expand these initial insights.

Transformations in academic work and faculty perceptions of artificial intelligence in higher education

Technologies based on artificial intelligence are transforming teaching practices in higher education. However, many university faculty members still face difficulties in incorporating these tools in a critical, ethical, and pedagogically meaningful way. This review addresses the issue of limited artificial intelligence literacy among educators and the main obstacles to its adoption. The objective was to analyze the perceptions, resistance, and training needs of faculty members in the face of the growing presence of artificial intelligence in educational contexts. To this end, a narrative review was conducted, drawing on recent articles from Scopus and other academic sources, prioritizing empirical studies and reviews that explore the relationship between intelligent systems, university teaching, and the transformation of academic work. Out of 757 records initially retrieved, nine empirical studies met the inclusion criteria. The most frequently examined tools were generative artificial intelligence systems (e.g., ChatGPT), chatbots, and recommendation algorithms. Methodologically, most studies employed survey-based designs and thematic qualitative analysis. The main findings reveal a persistent ambivalence: faculty members acknowledge the usefulness of such technologies, but also express ethical concerns, technical insecurity, and fear of professional displacement. The most common barriers include lack of training, limited institutional support, and the absence of clear policies. A shift in the teaching role is observed, with greater emphasis on mediation, supervision, and critical analysis of output generated by artificial intelligence applications. Additionally, ethical debates are emerging around algorithmic transparency, data privacy, and institutional responsibility. Effective integration in higher education demands not only technical proficiency but also ethical grounding, regulatory support, and critical pedagogical development. This review was registered in Open Science Framework (OSF): 10.17605/OSF.IO/H53TC.

Semantic and Visual Pathways to Artificial intelligence Literacy. Challenges and Lessons Learned in the Medical Domain

Artificial intelligence (AI) enhances medical research through greater accuracy and large-scale data handling. However, many healthcare professionals face methodological and reproducibility challenges due to limited AI training. Visual programming platforms help address this gap by enabling users to create machine learning (ML) workflows without extensive coding. This article analyzes such platforms, identifying key usability and educational challenges. Drawing on our experience developing a visual ML platform that uses an informal semantic representation approach, we propose improvements that leverage semantic representations—such as clearer workflow segmentation, interactive tutorials based on intuitive conceptual definitions, visual aids, and structured methodological checklists. By defining concepts and relationships through semantically inspired visual structures, these enhancements aim to improve user understanding, AI reliability, and research quality in medical contexts. Future research will evaluate their impact on AI education and application.

Challenges Faced by Teachers in Kuwait in Employing Artificial Intelligence in Teaching: Plans and Strategies for Effective Integration

With the rapid development of artificial intelligence (AI) technology, teachers in Kuwait face multiple challenges. This research explores these challenges and proposes strategies to address them. The qualitative approach was used through interviews with a sample of 30 department heads at the secondary level from various specializations. Then, the researchers applied an analytical descriptive curriculum to a random sample of 414 female public-school teachers in Kuwait. The results showed that fundamental challenges include inadequate preparation and qualification programs and increased concerns about insecurity and privacy in students' data. The study also mentioned no differences between female teachers' responses in Kuwait at a significance level of 0.05 based on their field of specialization. The study focused on female teachers' training courses on AI, where the result was that only 27.1% of female teachers took AI training courses. However, the number of women who did not train was 72.9%. These challenges had a larger impact on teachers. The study recommends providing professional development programs on AIED, developing academic guidance for AI in alignment with the vision of the Ministry of Education in Kuwait, establishing an AI awareness campaign to familiarize teachers with AI resources, and providing accessible resources for teachers. Received: 5 April 2025 / Accepted: 17 June 2025 / Published: 05 July 2025

Diagnóstico educativo con el uso de inteligencia artificial para la toma de decisiones

The article aims to provide a synthesis of the state of knowledge on academic management and the use of artificial intelligence in education, oriented towards educational diagnosis. The PRISMA protocol was used for the systematic review. The research is qualitative in nature, and the inclusion criteria were studies in the SciELO, Scopus, and Science Direct databases. Texts included in the period from 2020 to 2025, written in Spanish and English, were analyzed, and empirical research articles based on surveys, questionnaires, and/or interviews were excluded. As a result, the main studies that are taken as references for the present study were identified. The conclusions present the postulates to which the research adheres.

Turkish medical oncologists’ perspectives on integrating artificial intelligence: knowledge, attitudes, and ethical considerations

BackgroundIntegrating artificial intelligence (AI), especially large language models (LLM) into oncology has potential benefits, yet medical oncologists’ knowledge, attitudes, and ethical concerns remain unclear. Understanding these perspectives is particularly relevant in Türkiye, which has approximately 1340 practicing oncologists.MethodsA cross-sectional, online survey was distributed via the Turkish Society of Medical Oncology’s channels from October 16 to November 27, 2024. Data on demographics, AI usage, self-assessed knowledge, attitudes, ethical/regulatory perceptions, and educational needs were collected. Quantitative analyses were performed using descriptive statistics and graphics were generated using R v.4.3.1, and qualitative analysis of open-ended responses was conducted manually.ResultsOf 147 respondents (representing about 11% of Turkish oncologists), 77.5% reported prior AI use, mainly LLMs, yet only 9.5% had formal AI education. While most supported integrating AI into prognosis estimation, research, and decision support, concerns persisted regarding patient-physician relationships and social perception. Ethical reservations centered on patient management, scholarly writing, and research design. Over 79% deemed current regulations inadequate and advocated ethical audits, legal frameworks, and patient consent. Nearly all were willing to receive AI training, reflecting a substantial educational gap.ConclusionsTurkish medical oncologists exhibit cautious optimism toward AI but highlight critical gaps in training, clear regulations, and ethical safeguards. Addressing these needs could guide responsible AI integration. Limitations include a single-country perspective. Further research is warranted to generalize findings and assess evolving attitudes as AI advances.Trial RegistrationNot applicable due to cross-sectional survey design.

Enhancing AI literacy in undergraduate pre-medical education through student associations: an educational intervention

BackgroundThe integration of artificial intelligence (AI) into healthcare is rapidly advancing, with profound implications for medical practice. However, a gap exists in formal AI education for pre-medical students. This study evaluates the effectiveness of the AI in Medicine Association (AIM), an extracurricular program designed to equip pre-medical students with foundational AI knowledge.MethodsA quasi-experimental pretest-posttest control group design was employed, comparing knowledge acquisition between students participating in the AIM program (cohort group) and a control group of students not participating. The intervention spanned four weeks and included hands-on AI training, ethical considerations, data preprocessing, and model evaluation. Pretest and posttest assessments measured AI knowledge and pathology-related skills.ResultsParticipants in the AIM program demonstrated significant improvements in both AI knowledge and pathology-related scores. The cohort group showed a large effect size across all measured domains, particularly in pathology, with Cohen’s d values ranging from 1.83 to 4.74. Statistical analysis confirmed robust, significant improvements in test scores (t-test and Mann-Whitney U test, p < 0.001). There was no significant correlation between previous AI experience or attitudes toward AI and overall score improvement.ConclusionsThe AIM program effectively improved pre-medical students’ understanding of AI and its application in medicine, particularly in pathology. This study highlights the potential of extracurricular programs to address the need for AI education in medical curricula, especially in the pre-medical phase, and suggests that such initiatives could serve as a model for other institutions seeking to integrate AI education into healthcare training.

A critical AI media literacy framework: understanding layered bias and empowerment in artificial intelligence

ABSTRACT Researchers have increasingly called for a framework that critically engages Artificial Intelligence in Education (AIED). From the dual lenses of an education technology leadership and a critical media studies scholar, we analyze the challenges faced in education with the exponential uses of AI through a critical digital media literacy lens to create a Critical Media AI Framework. We provide an analysis of the current landscape of challenges in understanding layered bias in AI and strategic approaches to overcome these hurdles through a conceptual framework for AIED practices. Our framework provides conceptual understandings of AI and aligned critical questions. As our purpose is to offer a framework for scholars, instructors, and students to examine AI through a critical media studies lens, we bring together major concepts from seminal frameworks in our respective fields with novel guiding questions to reflect major overarching concepts that students must grasp about the non-neutrality of media as well as the layers of bias and elements of AI that make it unique from other types of media. Through critical questioning and understanding of these biases, we show that students can be empowered to be critical engagers and to reimagine the ways in which AI can be used.

Measuring Students’ Use of Digital Technology to Support Their Studies

To provide a more holistic understanding of how digital tools shape the educational environment, this paper includes a comprehensive analysis that explores several dimensions of technology use in higher education: use of artificial intelligence in education, online collaboration, use of an E-Board for learning, and excessive use of technology. With the aim of measuring students’ use of digital technology to support their studies, this research meets the goals of developing the measurement process, building a multi-criteria model, and applying it to a real-life example of determining the degree of students’ use of digital technology in relation to the demonstrated quality of academic performance. The analysis is based on a survey conducted among students at the University of Maribor’s Faculty of Economics and Business. Using factor analysis and multi-criteria evaluation, the findings reveal that students who demonstrate very-high-quality achievements also report the highest level of technology use to support their studies. They are followed by students with outstanding achievements, who excel in using an E-Board for learning and in demonstrating responsibility regarding excessive technology use. Students who achieve acceptable-quality results with certain defects stand out in online collaboration and the use of AI in the study process. The lowest level of technology use was reported by students demonstrating moderate-quality achievements. Theoretically, this research contributes to a better understanding of the multidimensional use of digital technology in higher education, while, practically, it provides useful guidelines for optimizing digital learning tools and enhancing the overall quality of the academic process.

Roles Of Artificial Intelligence in Education

Roles Of Artificial Intelligence in Education

Factors influencing AI adoption by Chinese mathematics teachers in STEM education

This study refines the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) to explore the factors influencing the adoption and utilization of artificial intelligence (AI) by Chinese mathematics teachers in STEM education, aiming to promote broader integration of AI within this domain. Utilising structural equation modelling (SEM) on survey data collected from 503 in-service mathematics teachers across China, the findings indicate that performance expectancy (PE), hedonic motivation (HM), and price value (PV) significantly affect teachers’ behavioural intention (BI). Moreover, the study finds that effort expectancy (EE), facilitating conditions (FC), and price value (PV) significantly influence teachers’ actual usage behaviour (UB). Notably, price value emerges as a crucial factor influencing both BI and UB, underscoring the importance teachers place on balancing the benefits of AI teaching tools with the time investment required for their adoption.