1 Iris Heung Yue Yim,
Artificial intelligence literacy in primary education: An arts-based approach to overcoming age and gender barriers,
Computers and Education: Artificial Intelligence,
Abstract: Artificial Intelligence (AI) literacy education was previously taught primarily at the university and secondary school levels but has recently started to be expanded to primary school settings. When available at the primary school level, AI literacy is often taught within computer science courses, which may potentially reinforce gender stereotypes and discourage female students’ engagement. In AI literacy education, the predominant teaching methods are constructivist approaches, which, while effective in fostering active learning, heavily emphasize technical skills and are therefore limited in their pedagogical scope, as they underplay other important questions, such as disinformation, data justice, and AI’s ethical and societal implications. The lack of a clear definition of AI literacy for primary education also raises questions about what to teach and how to teach it. Additionally, little attention has been devoted to understanding gender differences in learning outcomes within AI literacy primary education. This study advocates the use of an arts-based transdisciplinary approach for teaching AI literacy to 25 primary school students. A pilot study utilizing mixed methods was conducted to assess the effectiveness of this arts-based approach. Quantitative analysis through a paired t-test revealed a statistically significant improvement in AI literacy among students participating in knowledge tests. Moreover, the results of the Mann-Whitney and Kruskal-Wallis tests indicated that gender and age did not impact pre- and post-knowledge test scores. Qualitative analyses further revealed the pedagogical benefits of the arts-based approach, demonstrating that students enhance their conceptual understanding of AI literacy by reflecting on their artifacts. This study contributes to the literature by providing evidence with a small sample size that the arts-based approach can overcome age and gender barriers to accessing AI literacy education and can serve as a means to teach AI thinking.
Keywords: Artificial intelligence; AI literacy education; AI thinking; Primary school students; Arts-based pedagogy; Age and gender difference
2 Eun Mee Lim,
Metaphor analysis on pre-service early childhood teachers’ conception of AI (Artificial Intelligence) education for young children,
Thinking Skills and Creativity,
Abstract: This study explored PEC teacher’ (pre-service early childhood teachers’) conception of AI (Artificial Intelligence) education for young children by using metaphor analysis. Through exploring the conceptual metaphors of AI education for young children with 137 undergraduate students majoring in ECE (early childhood education) at I University in the central part of the United States, this present study tried to figure out the educationally meaningful direction of AI education for young children. The surveys were composed with open questions to find out PEC teachers’ metaphor of AI education for young children and they were distributed to research subjects. Collected data were named, classified, categorised, and finally, 7 categories out of 137 metaphors were analysed through the step of confirming. PEC teachers in this study had positive metaphorical concepts such as ‘possibility of play and experience’, ‘future essentials’, ‘innovation and change’, ‘convenience’, ‘assistant teacher’, and negative metaphorical concepts such as ‘double-sided meaning’, ‘Complexity’ of AI education for young children. PEC teachers’ beliefs about teaching-and-learning theory and methods related to AI education in ECE settings can be an important indicator of what thoughts and attitudes teachers will have when they implement the AI-related curriculum. Therefore, since PEC teachers have direct responsibility of planning and implementing AI education at ECE institutions, their conception of AI education in ECE would provide an important insight for shaping AI education for young children in the future.
Keywords: Artificial intelligence education; Pre-service teacher; Early childhood education; Metaphor analysis
3 Xiao Tan, Gary Cheng, Man Ho Ling,
Artificial Intelligence in Teaching and Teacher Professional Development: A Systematic Review,
Computers and Education: Artificial Intelligence,
Abstract: The application of Artificial Intelligence (AI) technology in education is increasingly recognized as a key driver of educational innovation. While extensive literature exists on the integration of AI technologies in educational settings, less emphasis has been placed on the critical role of teachers and their professional development needs. This study systematically reviews research conducted between 2015 and 2024 on teachers’ use of AI technology and their professional development, focusing on the relationship between the supply of professional development opportunities and the demand for AI integration among teachers. Using PRISMA principles and protocols, this review identified and synthesized 95 relevant articles. The findings reveal a significant imbalance in research focus. Specifically, 65% of the studies examined the application of AI in teaching, including technologies such as conversational AI and related technologies, AI-driven learning and assessment systems, immersive technologies, visual and auditory computing, and teaching and learning analytics. In contrast, only 35% of the studies explored AI’s role in enhancing teacher professional development. This review highlights a gap in research addressing the practical needs of teachers integrating AI technologies into their teaching practices. It emphasizes the need for future research to focus more on the potential of AI in teacher professional development and to investigate how AI technologies can be applied in education from both student learning and teacher teaching perspectives. Furthermore, research on AI in professional development should prioritize addressing technological ethical challenges to ensure responsible and effective integration.
Keywords: Artificial Intelligence (AI); AI in education; Systematic review; Teaching; Professional development
4 Sasithorn Chookaew, Pornchai Kitcharoen, Suppachai Howimanporn, Patcharin Panjaburee,
Fostering student competencies and perceptions through artificial intelligence of things educational platform,
Computers and Education: Artificial Intelligence,
Abstract: The growing demand for artificial intelligence (AI) skills across various sectors has enhanced AI-focused careers and shaped academic exploration in educational institutions. These institutions have been actively developing teaching methods that enhance practical AI applications, particularly through integrating AI with the Internet of Things (IoT), leading to the emergence of the Artificial Intelligence of Things (AIoT). This convergence promises significant advancements in AI education, addressing gaps in structured learning methods for AIoT. This study explored AIoT’s application in Smart Farming (SF) and its potential to enrich AI education and sectoral advancements. The AIoT platform was designed for SF simulations, integrating environmental sensing, AI processing, and user-friendly outputs. This platform was implemented with 40 first-year computer science university students in Thailand using a one-group pre-posttest design. This approach transformed theoretical AI concepts into experiential learning through interactive activities, demonstrating AIoT’s capability to increase AI conceptual understanding, trigger AI competencies, and promote positive learning perceptions. Therefore, this study presented the results as indicative of the AIoT platform’s potential benefits, emphasizing the need for further robust experimental research. This study contributes to educational technology discussions by suggesting improvements in AIoT platform effectiveness and highlighting areas for future investigation.
Keywords: AI education; Machine learning; Higher education; Arduino; Adaptive Neuro-Fuzzy
5 Nagaletchimee Annamalai, Brandford Bervell, Dickson Okoree Mireku, Raphael Papa Kweku Andoh,
Artificial intelligence in higher education: Modelling students’ motivation for continuous use of ChatGPT based on a modified self-determination theory,
Computers and Education: Artificial Intelligence,
Abstract: The purpose of this study was to investigate the determinants of higher education students’ motivation towards continuous usage of ChatGPT for English language learning, based on a modified Self-Determination Theory (SDT). A quantitative approach hinged on a cross-sectional survey design was adopted, and an online questionnaire used to collect data from 324 students studying English as Foreign Language (EFL) and English as a Second Language (ESL). The data were analyzed using a Partial Least Squares-Structural Equation Modelling (PLS-SEM) technique. This study established that initial ChatGPT usage determined students’ perceived autonomy, competence, relatedness and challenges in ChatGPT usage. In addition, a novel finding was that, both autonomy and relatedness predicted students’ competence in using ChatGPT to learn. Further, determinants of students’ motivation for continuous usage of ChatGPT were autonomy and relatedness. Lastly, the study through Important-Performance Map Analysis (IPMA), established autonomy as the most important as well as the highest performing factor determining students’ motivation for continuous usage of ChatGPT. The validated SDT model explained a large total variance of 70.8% in students’ motivation for continuous use of ChatGPT. Based on the results, recommendations were made for both theory as well as policy and practice towards ChatGPT usage in higher education.
Keywords: Artificial intelligence; ChatGPT; Higher education; Self-determination theory; Structural equation modelling
6 Héctor Galindo-Domínguez, Nahia Delgado, Lucía Campo, Daniel Losada,
Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education,
International Journal of Educational Research,
Abstract: With the recent integration of artificial intelligence (AI) in the educational field, understanding the variables that are related to teacher attitudes towards AI can be crucial for understanding their perspectives in the classroom. That is why the present study aimed to investigate whether Teacher Digital Competence is related to Teacher Attitudes towards AI, and if so, whether this relationship is moderated by the teacher’s educational stage, age, sex, years of experience, and field of knowledge. A total of 445 Spanish teachers from primary, secondary, and higher education participated in this study, responding to the Teacher Digital Competence Scale and the Teacher Attitudes towards AI Scale. The results revealed that, regardless of educational stage, sex, age, years of experience or field of knowledge, higher teacher digital competence is associated with a more positive teacher attitude towards AI. Moreover, high levels of willingness to use AI but low levels of personal experience with AI were found. Based on these results, it may be interesting to implement future interventions based on AI to enhance key dimensions of teacher digital competence, such as Information Management, Content Creation, and Problem-Solving. This could improve Teacher Digital Competence and subsequently enhance teachers’ perception of using artificial intelligence in the educational context.
Keywords: Teachers’ digital competence; Artificial intelligence; Teacher attitudes; Higher education; Primary education; Secondary education
7 Iris Heung Yue Yim,
A critical review of teaching and learning artificial intelligence (AI) literacy: Developing an intelligence-based AI literacy framework for primary school education,
Computers and Education: Artificial Intelligence,
Abstract: Artificial intelligence (AI) literacy education mainly targets secondary and university students, often overlooking the unique needs of younger students. This gap in AI literacy primary school education presents theoretical and pedagogical challenges. Despite the pervasive influence of AI, which can exacerbate inequalities and raise ethical challenges, primary students often lack an understanding of AI principles and mechanisms. Recent developments in age-appropriate AI learning tools have extended AI literacy to primary schools, but AI literacy frameworks for this age group remain underdeveloped. This study aims to conceptualize AI literacy by analyzing existing theoretical frameworks and proposing a new inclusive AI literacy framework for young students. A scoping review is employed using four credible index databases, and 19 articles are selected, with 17 AI literacy frameworks identified across all educational levels, from early childhood to university. This study reveals that the predominant methodologies for developing AI literacy frameworks involve empirical research studies and literature reviews, adhering to national government or institutional standards. These frameworks commonly incorporate 1) Bloom’s taxonomy or a similar progression framework, such as Use-Create-Modify, 2) constructionism, and 3) computer science perspectives such as theories of computation. The findings reveal that AI literacy is situated at the intersection of digital literacy, data literacy, computational thinking, and AI ethics, emphasizing the need for a transdisciplinary and interdisciplinary approach that encompasses both technological and societal impacts. However, the study argues that the current paradigms of AI literacy frameworks for young students often emphasize constructionist perspectives without fully considering the interactions between human and technological agents. This gap highlights the necessity for a new conceptual framework that acknowledges both human and non-human agents in AI literacy education for young students. The research contributes by conceptualizing AI literacy and guiding policymakers and curriculum designers to implement holistic AI literacy education for young students.
Keywords: Artificial intelligence; AI literacy; Intelligence-based AI literacy framework; AI thinking; Primary school students; Systematic review; AI learning and teaching
8 Yuhan Liu, Heng Zhang, Meilin Jiang, Juanjuan Chen, Minhong Wang,
A systematic review of research on emotional artificial intelligence in English language education,
System,
Abstract: In learning English as a foreign language (EFL), students often experience foreign language anxiety. Artificial intelligence (AI) applications that provide emotional support and/or create emotional impacts on student learning, so-called emotional AI applications, have received increased attention. However, there is a lack of a systematic review of studies on emotional AI in EFL education. This paper presents a systematic review of research in this field. The results reveal five affordances of emotional AI in EFL education, namely (1) enabling human-like conversations, (2) providing personalized real-time feedback or instructions, (3) translating images into English text, (4) generating personalized learning content and tasks, and (5) recognizing and analyzing emotions. The first three affordances are more frequently used and have shown promising effects on improving students’ behavioral, cognitive, and affective learning outcomes. Moreover, the findings reveal that emotional support is often integrated with cognitive support; providing emotional support alone may not be enough to support student learning. Meanwhile, providing cognitive support alone can enhance both affective and cognitive learning outcomes. Finally, attention should be paid to the factors that might influence the adoption and effects of emotional AI in EFL education.
Keywords: Language education; English as a foreign language (EFL); Artificial intelligence; Emotion; Systematic review
9 Judit Martínez-Moreno, Dominik Petko,
What motivates future teachers? The influence of Artificial Intelligence on student teachers’ career choice,
Computers and Education: Artificial Intelligence,
Abstract: Artificial Intelligence in Education (AIEd) is reshaping not only the educational landscape but also potentially influencing the motivations of aspiring teachers. This paper explores whether considerations related to AIEd play a role in student teachers’ decision to become teachers. For this aim, the study introduces a new AI subscale within the (D)FIT-Choice scale’s Social Utility Value (SUV) factor and validates its effectiveness with a sample of 183 student teachers. Descriptive statistics reveal high mean scores for traditional motivators like Intrinsic Value Teaching, while AI-related factors, although considered, exhibit lower influence. A noticeable disconnection exists between digital motivations and the aspiration to shape the future, suggesting a potential gap in student teachers’ understanding of digitalization’s future impact. An extreme group analysis reveals a subset of student teachers who significantly consider AI. This group also gives value to Job Security and Make a Social Contribution, suggesting an awareness of AI’s societal and professional impacts. Based on these findings, it is recommended to put a focus on teacher education programs to ensure student teachers’ understanding of the impact of AI on education and society.
Keywords: AI; AIEd; Career choice; (D)FIT-Choice; Digital transformation; Student teachers; Motivations; Agency; Teacher education
10 Lingjie Yuan, Xiaojuan Liu,
The effect of artificial intelligence tools on EFL learners’ engagement, enjoyment, and motivation,
Computers in Human Behavior,
Abstract: The use of Artificial Intelligence (AI) tools has become increasingly prevalent within higher education and numerous studies have indicated the efficacy of AI applications within the English as a foreign language (EFL) context. Enhancing student engagement, foreign language enjoyment (FLE), and motivation in the field of AI education are significant in promoting effective learning outcomes. The rise of AI in educational settings has led to a paucity of research exploring its impact on various educational constructs. This quasi-experimental research examined the role of utilizing an AI tool, specifically Duolingo, on the engagement of FLE, and motivation of Chinese EFL learners. The present study involved 383 out of 412 Chinese university EFL students, who were assigned to either the experimental or control group. Both groups participated in pre- and post-intervention assessments prior to and following a 12-session intervention. Running a One-way Analysis of Covariance (ANCOVA) revealed statistically significant improvements in FLE, engagement, and motivation within the experimental group. On the contrary, the control group demonstrated a minor alteration in the aforementioned variables. AI tools possess the capability to captivate the attention of students and consequently enhance their motivation to actively participate in the educational experience. The results of the study designate that there are significant implications for both teachers and teacher educators concerning the incorporation of AI tools in the EFL classroom environment.
Keywords: Artificial intelligence tools; EFL learners; Engagement; Enjoyment; Motivation