Research Article
Mailizar Mailizar, Ega Gradini, Tommy Tanu Wijaya, Abdulsalam Al-Manthari
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep590
ABSTRACT
This study aims to examine the factors that influence university students’ behavioral intention in accepting video-conferencing for learning mathematics in the post-COVID-19 era. Video-conferencing as a learning tool has been widely used to establish effective communication among learners, teachers, and peers. This is particularly valuable in situations where face-to-face communication is not possible, such as during the COVID-19 pandemic. As the pandemic comes to an end, it is unclear whether video-conferencing will continue to be widely used as a teaching and learning tool. This study extends the technology acceptance model (TAM) by adding digital literacy (critical use, technology focused, and digital reading) and social presence (SC) as external factors. Extended TAM with digital literacy and SC as factors provide understanding on how university students’ digital literacy level and their connection and engagement with others influences their acceptance of video-conferencing for learning. The participants include 238 students from six universities in Indonesia (three public universities and three private universities), who enrolled in bachelor’s degree of mathematics and mathematics education. The questionnaire consisted of 40 items administered online to students selected by random sampling. A partial least square-structural equation modelling approach was used to analyze the measurement model, test the path, and draw conclusions. The findings of this study shows that attitude is the most important factor that has influenced the intention to employ video-conferencing after a pandemic situation. In addition, SC is considered the most significant factor in students’ attitudes towards the use of video-conferencing in the post-COVID-19 era.
Keywords: TAM, video conferencing acceptance, post-COVID-19, university student
Research Article
Liqaa Habeb Al-Obaydi, Marcel Pikhart, Md Kamal Hossain
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep591
ABSTRACT
Chatbots with artificial intelligence (AI) capabilities, such as ChatGPT, are an important tool for language learning because they provide an interactive and adaptive learning environment that can be adjusted to each learner’s level of skill. This paper explores the application of ChatGPT, an advanced AI-powered chatbot, as a tool for language learning, focusing on the development of core language skills such as speaking, reading, writing, listening, and communication. The study involves 30 English as a foreign language college students from two different higher education contexts, Iraq and the Czech Republic. The research utilizes two methods; content analysis by asking direct questions and using ChatGPT as a guide to recommend actions is the second phase, followed by an exercise partner and rater. Jigsaw technique is also used to assign language skills to the groups of participants to consult ChatGPT then share and evaluate its effectiveness. A phenomenographic research design using an interview was used to gather data for this study, which accurately reflects how a group of people views a certain phenomenon. Following the six-week experiment, the participants are interviewed to get their thoughts on using ChatGPT to improve language skills. The findings indicate that ChatGPT’s content analysis method has limited effectiveness in developing language skills due to the kind of output gained which is only instructions and/or recommendations. However, using ChatGPT as a guide and then as a partner offers very big support for students in all language skills, with varied results influenced by the prompts used. Students generally have positive attitudes toward using ChatGPT, finding it essential for developing their speaking (75%), writing (100%), listening (87%), reading (87%), and communication (75%). The study suggests that ChatGPT may be more advantageous for students if it is used in regular language practice for a long period of time.
Keywords: ChatGPT, foreign language learning, core language skills, second language acquisition, digital learning, chatbots, artificial intelligence in education
Research Article
Valentine Joseph Owan, Ibrahim Abba Mohammed, Ahmed Bello, Tajudeen Ahmed Shittu
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep592
ABSTRACT
Despite the increasing interest in artificial intelligence technologies in education, there is a gap in understanding the factors influencing the adoption of ChatGPT among Nigerian higher education students. Research has not comprehensively explored these factors in the Nigerian context, leaving a significant gap in understanding technology adoption in this setting. This study addressed this gap by investigating the predictors of students’ behavioral intentions (BIs) and actual use behavior of ChatGPT through the lens of the unified theory of acceptance and use of technology 2 (UTAUT2) framework. A cross-sectional correlational research design was used to examine the relationships between extended UTAUT variables, BIs, and ChatGPT use behavior. A sample of 8,496 higher education students from diverse institutions in Nigeria participated in the study. The data were collected using the higher education students’ ChatGPT utilization questionnaire, which assessed various factors, such as performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FCs), hedonic motivation (HM), habit (HB), BI, and ChatGPT use behavior. The findings reveal several significant predictors of students’ BIs and actual usage of ChatGPT. PE, SI, HM, and HB were found to be significant positive predictors of BI, while EE and FCs were significant negative predictors. For ChatGPT use behavior, FCs, HM, HB, and BI were significant positive predictors, whereas PE and SI were significant negative predictors. BI mediated the relationships between several factors and ChatGPT usage behavior: positively for some (PE, SI, HM, and HB) and negatively for others (EE and FC). This study contributes to understanding the adoption of ChatGPT in higher education contexts. The findings highlight the importance of addressing usability issues, providing adequate support and resources, promoting a positive user experience, fostering habitual usage, and leveraging social networks to encourage adoption.
Keywords: artificial intelligence, chatbots, GenAI, large language models, technology use
Review Article
Jiayan Xie, Ghayth K. S. Al-Shaibani, Jingli Zhao, Othman Talib
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep593
ABSTRACT
The application potential of the metaverse as an emerging technology in education has sparked widespread attention globally. A systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses method, which yielded 43 relevant articles from a total of 527 articles to examine the current status and the potential of the metaverse adoption in education. The analysis involves seven aspects as reflected in the seven research questions posed. The results indicate global widespread interest in exploring the potential application of the metaverse in education, primarily focusing on general educational settings and relatively less on specific subjects such as English and mathematics. Surveys and interviews were the primary research methods used, with the technology acceptance model and the unified theory of acceptance and use of technology as common theoretical frameworks. Twenty-five factors influencing the adoption of the metaverse in education have been identified, and six future research topics, such as examining students’ and teachers’ behaviors and attitudes have been proposed. This review contributes to constructing a novel framework for future research of the metaverse adoption in education, encouraging educators to integrate its advantages into specific subjects based on students’ needs for quality education.
Keywords: the educational metaverse, the metaverse adoption framework, contributing factors, quality education
Research Article
Najah S. Alsaedi
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep594
ABSTRACT
This study employed the technology acceptance model to explore Saudi undergraduates’ perspectives on using ChatGPT for English as a foreign language learning. It also aimed to explore the impact of gender, academic level, and experience with ChatGPT on their perceptions. A descriptive quantitative approach was used, using a web-based questionnaire for data collection. The results indicated that students perceived ChatGPT as useful and easy to use, and they demonstrated strong behavioral intentions to use it. However, they expressed concerns about potential overreliance and academic dishonesty. The analysis showed no significant gender differences in perceived usefulness (PU), negative attitudes, or behavioral intention; however, females reported slightly higher positive attitudes and greater ease of use compared to males. The results also indicated that students’ perceptions varied based on their academic level, but there were no differences in perceived ease of use and behavioral intention. This suggests a consistent recognition of ChatGPT’s usability and intention to use across different academic levels. The findings also revealed that students’ familiarity and experience with ChatGPT significantly enhanced its PU, ease of use, and overall acceptance. The detailed findings and their implications for both practical applications and policymaking are discussed.
Keywords: technology acceptance model, ChatGPT, artificial intelligence, Saudi students, higher education
Research Article
Mariam Hejab Alshaibani, Waleed Mugahed Al-Rahmi, Ragad M. Tawafak
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep595
ABSTRACT
This study introduces and validates the artificial intelligence anxiety scale (AIAS), a novel instrument designed to measure researchers’ anxieties when employing artificial intelligence (AI) tools in academic writing. As AI technologies rapidly infiltrate scholarly work, however, the primary concern grows about their ethical implications, impact on traditional research skills, and the lack of institutional readiness issues that remain underexplored in existing literature. Addressing this critical gap, the AIAS offers a novel framework grounded in real-world academic concerns. Using an inductive approach, data were collected from 219 faculty members and graduate students at Taif University, Saudi Arabia, revealing four core dimensions of AI-related anxiety: (1) concerns about the accuracy of AI outputs, (2) fear of committing plagiarism, (3) lack of institutional guidelines, and (4) fear of losing research skills. Exploratory and confirmatory factor analyses confirmed the existence of these factors, and reliability testing indicated robust internal consistency. By offering the first validated tool specifically tailored to measure AI-related anxiety in academic contexts, this study provides a significant resource for researchers and institutions. Its application can guide universities in devising training and support initiatives to ensure the ethical and practical integration of AI, thereby sustaining the integrity of traditional research competencies and enhancing the overall quality and credibility of academic endeavors.
Keywords: AI anxiety, confirmatory factor analysis, academic research, psychological assessment
Research Article
Thada Jantakoon, Thiti Jantakun, Kitsadaporn Jantakun, Weerapa Pongpanich, Rungfa Pasmala, Panita Wannapiroon, Prachyanun Nilsook
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep596
ABSTRACT
The increasing integration of artificial intelligence (AI) in education has raised significant questions about its pedagogical value, especially in language learning. This meta-analysis examines the extent to which AI contributes to the development of English-speaking and listening skills. A systematic review of the literature was conducted by the preferred reporting items for systematic reviews and meta-analyses guidelines, utilizing peer-reviewed studies indexed in Scopus, ERIC, and EBSCOhost from 2017 to 2024. Nineteen studies met the inclusion criteria, all of which utilized experimental or quasi-experimental designs with measurable learning outcomes. The analysis reveals a substantial overall effect of AI-enhanced instruction (standardized mean difference [SMD] = 0.981, 95% confidence interval [0.571, 1.391], p < .001), with particularly notable improvements in speaking proficiency (SMD = 1.033). Although listening outcomes showed a positive trend (SMD = 0.714), the effect did not attain statistical significance. Considerable heterogeneity was noted across the studies, reflecting variations in learner populations, instructional contexts, and AI applications. Quality appraisal using the risk of bias in non-randomized studies of interventions framework indicated a predominantly low to moderate risk of bias. Publication bias analysis, including funnel plot symmetry and fail-safe N, further confirmed the reliability of the results. These findings highlight the advantages of AI in enhancing speaking skills within English instruction and underscore the need for further empirical studies to investigate its impact on listening comprehension. Collectively, the results provide timely, evidence-based guidance for educators and policymakers aiming to integrate AI effectively into language education. Highlight the advantages of AI in enhancing speaking skills within English instruction and underscore the need.
Keywords: artificial intelligence, English instruction, speaking skill, listening skill, meta-analysis
Research Article
Osama Taani, Khaleel AlArabi, Hassan Tairab, Othman Abu Khurma, Badriya AlSadrani
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep597
ABSTRACT
Research on artificial intelligence (AI) in education has revealed a significant gap in understanding the complex relationship between AI, self-regulation, and teachers’ pedagogical practices in the United Arab Emirates (UAE). The study adopts a cross-sectional study that uses exploratory survey aimed at investigating ChatGPT’s role in the future of UAE education by examining (1) how in-service teachers use ChatGPT in daily teaching, (2) its impact on their self-regulation abilities, and (3) potential changes in teaching practices resulting from ChatGPT integration. Data were collected from 311 in-service teachers from public and private schools, using a self-report questionnaire. The findings showed disparities in the perspectives of participants at both public and private schools, as well as gender levels. Public school teachers mainly used ChatGPT to generate instructional materials and facilitate classroom dialogue, whereas private school teachers focused on its application in lesson design. Private school teachers found ChatGPT easier and more effective than their public counterparts. Private school male teachers outperformed public school males by 0.56 points, and private school female teachers scored 0.52 points higher than their public-school counterparts. Regardless of educational background, private school teachers experienced better self-regulation with ChatGPT than public school teachers. The impact of ChatGPT on self-regulation varied according to teaching experience, affecting novice and experienced teachers differently from those with moderate experience. However, the findings on the impact of age are inconclusive and require further research. These results are discussed in the context of the current AI integration policies in UAE education.
Keywords: self-regulation, ChatGPT, in-service teachers, science education, UAE
Review Article
Huu Tong Duong, Minh Dung Tang, Xuan Mai Vo, Tien-Trung Nguyen, Muhammad Noor Kholid, Nga Thi Hoang, Phuong Uyen Bui, Minh Triet Viet Le
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep598
ABSTRACT
Many studies have been conducted on in-service mathematics teachers’ beliefs and instructional practices regarding integrating information and communication technologies (ICTs) into teaching. This systematic review study investigates in-service mathematics teachers’ beliefs, instructional practices, and the relationship between them in integrating ICTs into their instruction. Based on the systematic review methodology and the guidelines for preferred reporting items for systematic reviews and meta-analyses, with descriptive statistics and qualitative analysis data processing methods, 15 studies published from 2014 to 2023 were selected to perform the analysis. These results indicate that many studies show teachers’ positive beliefs about ICTs, including their ability to facilitate teaching and learning, improve students’ understanding, and promote student-centered learning. At the same time, teachers use multiple forms of technological tools in many different teaching activities. However, difficulties in practice, from teachers’ technological skills and knowledge to pedagogical skills in classroom management, are also analyzed from selected studies. Furthermore, this study shows that the conclusions about a correlation between beliefs and instructional practices in integrating ICTs into teaching in previous studies are relatively diverse. From these results, the study offers recommendations to stakeholders on professional development for in-service mathematics teachers, indicates its limits, and identifies potential future research areas.
Keywords: beliefs, ICTs, in-service mathematics, teachers, instructional practice, systematic review
Research Article
Akmarzhan Nogaibayeva, Gaukhar Yersultanova
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep599
ABSTRACT
This study explores secondary school teachers’ perspectives on artificial intelligence (AI)-supported tools through qualitative in-depth interviews with 16 teachers of English as a foreign language in Kazakhstan. The research aimed to understand teachers’ views on pedagogy, their knowledge of AI, and their perceptions of its opportunities and limitations. Findings revealed that teachers were largely pragmatic and open to adopting new digital tools; however, concerns arose regarding scenarios where AI might diminish the teacher’s role. The practical implication of the study suggests that AI-supported tools should be framed as context-specific contributors to learning, rather than as transformative innovations. The qualitative approach was chosen to address the challenge of limited teacher familiarity with AI, enabling interviewers to clarify concepts and provide demonstrations during discussions. Kazakhstan’s bilingual and trilingual education system provided a unique context for this study. Education reform, often associated with technology, has been a focus in the post-Soviet era, though the system remains relatively centralized compared to OECD countries. Notably, better-resourced Nazarbayev Intellectual Schools serve as a model for trilingual secondary education. Teachers reported access to continuing professional development opportunities and ICT-related training, with classrooms typically equipped with projection devices, IT rooms, and sometimes tablets. Students’ use of mobile phones in schools was regulated but permitted for specific educational purposes. This research highlights the importance of positioning AI tools as augmentative technologies, emphasizing their role in supporting rather than replacing teachers in enhancing educational outcomes.
Keywords: AI, language learning, teacher perspectives, teaching and learning
Research Article
Filomachi Spathopoulou, Konstantinos M. Pitychoutis, Stavros Papakonstantinidis
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep600
ABSTRACT
The rapid advancement of artificial intelligence (AI) is transforming higher education, impacting pedagogical practices, administrative processes, and faculty engagement with technology. While AI holds promise to enhance learning and streamlining operations, its adoption remains complex and debated. This study examines faculty perceptions of AI integration, focusing on factors such as teaching experience, institutional context, and disciplinary specialization. Using a quantitative survey, the research explores AI engagement across institutions and disciplines, analyzing how demographic factors influence adoption. Findings suggest that junior faculty and those in technology-driven environments demonstrate higher AI confidence and adoption, whereas senior faculty engage in AI leadership yet express skepticism about its pedagogical applications. Disciplinary differences reveal that faculty in content-based fields view AI as a teaching tool, while those in applied disciplines utilize it more strategically for administrative and leadership functions. The study also addresses ethical and institutional challenges, including concerns over data privacy, algorithmic bias, and institutional readiness. By identifying these barriers, the research highlights strategies for fostering AI literacy, professional development, and ethical implementation in higher education. This study contributes to the discourse on AI in academia by presenting an educator-centered perspective, bridging the gap between technological advancement and pedagogical practice. The findings provide academic leaders and policymakers with insights on creating AI-inclusive environments that align with faculty needs, uphold ethical standards, and enhance student learning outcomes.
Keywords: artificial intelligence, faculty perceptions, pedagogical practices, AI adoption, ethical challenges, institutional readiness
Research Article
Nivine Aziz, Rayya Younes
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep601
ABSTRACT
Today’s generation exhibits a strong affinity for video games, presenting an opportunity to harness this interest for educational purposes. Integrating gaming elements into education has the potential to captivate students while improving information retention. Virtual reality (VR), in particular, offers a transformative approach by creating immersive and interactive learning experiences. In our study, we investigated the feasibility of integrating VR into Lebanese classrooms (K-12). Data was collected through an online survey with 110 respondents and interviews with 14 educators. Findings reveal that educators are enthusiastic about VR’s potential but express caution about its implementation. While acknowledging benefits such as enhanced student engagement, improved retention, and alignment with student preferences, they also highlighted challenges, including physical side effects, the need for teacher training, extensive preparation, and classroom management complexities. Additionally, educators proposed a comprehensive list of activities that could effectively utilize VR in educational settings, reflecting a balance between innovation and practical application. These insights underline the need for further exploration and structured planning to successfully integrate VR into the Lebanese educational system.
Keywords: virtual reality, Lebanon, challenges, opportunities, educators’ perception
Research Article
Karim Ragab, Enrique Martínez-Jiménez, Elvira Fernandez-Ahumada
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep602
ABSTRACT
This study explores students’ perceptions of their engagement with interactive technologies in STEM classes at an applied technology school in the United Arab Emirates (UAE). Specifically, the interactive technology (IT) involves artificial intelligence and virtual reality, along with collaborative digital platforms that incorporate AI elements such as Google Docs, Microsoft Teams, and Nearpod. The focus is on understanding how students perceive changes in cognitive, social, reflective, and goal-oriented engagement after using these technologies. Utilizing a mixed-methods approach, the study involved 126 male students from grades 9-12. The quantitative phase employed a pre-post survey with paired sample t-tests and repeated measures analysis of variance to assess changes in engagement. In contrast, the qualitative phase included focus group discussions to explore student perceptions. The results revealed that students perceived a significant improvement in various dimensions of engagement, including cognitive, social, reflective, and goal-oriented engagement, post-intervention following the use of IT. The findings suggest that interactive technologies can positively shape students’ learning experiences, as these technologies cater to diverse student needs, improving academic outcomes and providing a more fulfilling educational experience. The results align with the UAE’s educational goals of fostering a knowledge-based economy through innovative practices.
Keywords: interactive technology, student engagement, stem education, artificial intelligence, virtual reality
Research Article
Jaitip Nasongkhla, Chich-Jen Shieh
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep603
ABSTRACT
This study examines the effect of robot-assisted problem-based learning (PBL) on learning outcomes in higher education. A quasi-experimental design was adopted, and the application was carried out over 16 weeks (48 hours) with two parallel branches. In the experimental group, a question-and-answer-focused educational robot was integrated into the project-based learning process, while in the control group, the same content was applied without robots. Measurements encompassed self-directed learning (including self-management), problem-solving, and comprehension/discovery dimensions. Bayesian comparisons showed that the level of evidence in favor of the experiment was in the medium-strong range for most variables; a significant increase was observed particularly in self-management. Problem-solving and comprehension/discovery scores were also higher in the experimental condition. Figures and tables report posterior effect sizes and 95% confidence intervals. The results indicate that robot-assisted PBL, which offers timely and elucidative feedback, improves the metacognitive processes associated with PBL and aids students in adhering to their study goals more consistently. In conclusion, robot-assisted PBL can significantly improve self-management, problem-solving, and comprehension outcomes, provided that appropriate feedback design and application fidelity are maintained. Future research should examine its effects across various fields by integrating long-term follow-up periods and behavioral performance assessments. The study enhances the reproducibility of results by evaluating the strength of evidence through open Bayesian reporting. This method makes design choices more transparent and establishes clear guidelines on how educational technologies should be used responsibly.
Keywords: problem-based learning, AI robots, blended learning, learning outcomes, self-directed learning
Review Article
Regina G. Sakhieva, Elena E. Grishnova, Marina A. Zhukova, Ekaterina G. Sokolova, Natalia I. Lapidus, Oleg V. Khlusyanov
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep604
ABSTRACT
The use of information and communication technologies (ICT) in higher education significantly improves the quality of teaching for learners. However, no bibliometric studies have analyzed the use and acceptance of ICT in higher education in the relevant literature. The aim of this paper is therefore to analyze trends and patterns related to ICT in higher education from 2005 to 2024. The data was collected from the Scopus database. The authors analyzed 4,348 articles. The results indicate that the number of publications has increased continuously from 2008 to 2024. The results also show that the journal Computers and Education published the most cited articles and that the most cited authors are from developed countries. The findings concerning most of the co-cited documents showed a wide range of methodological and theoretical approaches to ICT in higher education. Finally, the analysis of research topics over the last 20 years revealed three clusters and shows that ICT plays a crucial role in areas such as change and innovation in education, development of clinical competencies in medical education, and the rapid adoption of distance learning in the wake of the COVID-19 pandemic. These findings can guide research on technology adoption by higher education students.
Keywords: information and communication technologies, ICT, higher education, bibliometric analysis, research trends
Research Article
Merih Welay Welesilassie, Berhane Gerencheal, Seifu Berihu
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep605
ABSTRACT
Digital inclusion is crucial for providing equitable educational opportunities for students with disabilities. However, significant barriers remain prevalent in low-resource contexts. This study is a beacon of understanding, examining the acceptance, perceptions, and obstacles related to educational technology engagement among young Ethiopian students with physical disabilities. Utilizing an explanatory sequential mixed-methods design, quantitative data were collected from 87 students, followed by in-depth interviews with 12 participants to gain deeper insights into their experiences. The results indicated a strong acceptance and perceived usefulness of digital tools for promoting independent learning and productivity. Nevertheless, students encountered substantial challenges, including a lack of digital readiness support, inadequate infrastructure, high costs, and insufficient teacher training. Structural equation modeling analysis revealed that digital readiness support, acceptance, and trust were significant predictors of students’ willingness to adopt digital technologies. Qualitative findings further illuminated socio-cultural barriers, such as stigma and social exclusion, which hinder effective digital engagement. Utilizing the technology acceptance model in conjunction with the digital divide theory, the findings of this study provide significant insights into the challenges of digital access in Ethiopian educational institutions. The proposed inclusive framework seeks to address critical infrastructural and cultural barriers, thereby promoting equity for students with disabilities. This research underscores the importance of developing and implementing strategies that enhance digital accessibility and inclusion within the educational landscape.
Keywords: digital inclusion, students with disabilities, technology acceptance model, digital divide theory, inclusive education
Research Article
Kiran Fahd, Shah J. Miah
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep606
ABSTRACT
The increasing reliance on the learning management system (LMS) in this era of digital education offers a vital source of student data that can be leveraged to predict student academic progress. Predicting student academic progress in higher education (HE) supports timely intervention and enhances student retention. This study develops and compares multiple machine learning (ML) and deep learning (DL) models to identify at-risk students based on students’ interaction data with LMS by leveraging an integrated DSR methodology. Multiple predictive models are developed by incorporating data augmentation and balancing techniques to address class imbalance and enhance the accuracy of the predictive model. The study compares ten different models to achieve the highest classification accuracy in predicting students at risk of failing through the integration of through the integration of both ML and DL algorithms, including random forest, decision tree, convolutional neural networks, multi-layer perceptron, and long short-term memory (LSTM). The comparison results unscored the value of the DL based predictive model in the HE setting to precisely predict student academic performance, particularly the LTSM based model, which has the highest and nearly perfect accuracy. The existing LMS systems can incorporate this DL based predictive model to provide educational stakeholders with benefits and insights that support students’ academic journeys and institutional success.
Keywords: machine learning, deep learning, design research, higher education, LMS data, student academic progress
Research Article
Marina A. Zhukova, Sergei P. Zhdanov, Tatiana A. Baklashova, Natalya Y. Garnova, Olga V. Pashanova, Irina I. Kruse, Albina R. Shaidullina
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep607
ABSTRACT
The aim of this study is to develop a valid and reliable instrument that measures pre-service teachers’ readiness and attitudes towards online teaching practice. The sample of the study consisted of 411 pre-service teachers studying at a Russian university. 87.7% of the participants were female and 12.3% were male. When the age groups are analyzed, the 18-19 age group is 33.3%, the 20-21 age group is 32%, the 22-23 age group is 22.9% and the 24 and over age group is 11.8%. 83.2% of the participants had previous online teaching experience. During the scale development process, expert opinion was obtained and validity and reliability analyses were conducted. Exploratory factor analysis and confirmatory factor analysis revealed that the scale had a three-factor structure: online teaching self-efficacy (11 items), online professional support and collaboration (8 items), and attitude towards online teaching and perception of development (5 items). This three-factor structure explained 67.1% of the total variance. The reliability coefficients of the scale ranged between 0.841 and 0.956 for the sub-dimensions. As a result of the latent profile analysis, the participants were categorized into six different profiles: competent and enthusiastic group (37%), high performance group (20%), collaboration-focused group (1%), moderate adaptation group (29%), low motivation group (4%), and open to development group (10%). The findings show that the developed scale is a valid and reliable tool that can be used to assess pre-service teachers’ online teaching readiness and attitudes.
Keywords: online practicum, teacher education, scale development, readiness, attitudes, digital society
Research Article
Dana Ondrušková
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep608
ABSTRACT
This study examines the effectiveness of pronunciation training by comparing two instructional methods: native speaker-led instruction and artificial intelligence (AI)-driven pronunciation tools. The research aims to determine whether AI-assisted pronunciation training can serve as an effective alternative or complement to traditional instruction. This is particularly relevant for language programs where access to native speakers is limited. The study employs a quantitative research design, analyzing pronunciation accuracy, intelligibility, and listening comprehension among A1-level French learners. A statistical comparison of learners’ performance in both groups was conducted, including Chi-square tests and standard deviation analysis. The findings suggest that AI-based pronunciation training is at least as effective as native speaker-led instruction, with students in the CAPT group performing even better, particularly in intelligibility and intonation. The reduced performance variability among AI-trained learners suggests that these tools provide a structured and uniform learning experience. These insights contribute to optimizing pronunciation teaching methods, highlighting the potential of AI as a scalable and accessible pronunciation training solution in foreign language education.
Keywords: pronunciation training, AI-assisted learning, language education, listening comprehension
Review Article
Alfiya R. Masalimova, Yuliya P. Kosheleva, Aleksandr I. Burov, Olga V. Payushina, Natalia L. Sokolova, Maria A. Khvatova
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep609
ABSTRACT
This study maps 417 peer reviewed publications (2022-2025) at the intersection of sustainability education (SE) and artificial intelligence (AI) using bibliometric methods. We chart venues, co authorship, keyword evolution, and technique usage. The results reveal that “ChatGPT” and “generative AI” are becoming the most popular terms after 2022. Outputs are still mostly from North America and Europe, although contributions from Saudi Arabia, India, and Malaysia are growing. Institutional networks are broken, which means that institutions don’t cooperate together very often. Supervised learning predominates, and neural networks are the most used single technique. We synthesize scattered findings into three practical principles–personalization–protection, competence alignment, and multi-level synchronization–that link AI uses to core SE competencies and support course to institution coordination. The study also shows a dual sustainability lens: AI can help fight climate change, but it also has implications for privacy and the environment. This shows the need for energy reporting and bias safeguards. We suggest causal and longitudinal assessments, collaborative datasets and rubrics, and capacity enhancement for resource-limited environments. Some of the problems are a short citation window (2022-2025), a bias against English speakers, and the possibility of missing databases. Overall, the subject is growing swiftly, but it requires more proof, common standards, and more environmentally friendly ways of doing things to turn AI into lasting educational value.
Keywords: sustainability education, artificial intelligence, generative AI, bibliometric analysis
Research Article
Ana Callado, Isabel Saúde, José Luís Araújo
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep610
ABSTRACT
The rapid spread of generative artificial intelligence (GenAI) in contemporary society calls for a rethinking of education. This study investigates Portuguese secondary students’ perceptions of chat generative pre-trained transformer (ChatGPT) in education. In 2024, we surveyed 114 students (aged 16-17) from one school in 2024 using a 15-item questionnaire (closed- and open-ended items) covering five dimensions: (1) knowledge of AI and ChatGPT; (2) use of ChatGPT; (3) perceived accuracy of ChatGPT-generated outputs; (4) the potential of ChatGPT for teaching; and (5) ethics and academic integrity. Responses to closed-ended items were analyzed descriptively and open-ended responses underwent content analysis. The results indicate that, although most students are familiar with AI and ChatGPT, their demonstrated knowledge is largely utilitarian and superficial. Students predominantly use ChatGPT for schoolwork and attribute high reliability to its outputs, often without the necessary critical evaluation. We also identified weaknesses in their ethical understanding, particularly regarding academic integrity and plagiarism. We argue for critical AI literacy and teacher professional development to support pedagogically grounded and ethical integration of GenAI. Education for the ethical and responsible use of these technologies proves essential to prepare young people for future challenges.
Keywords: ChatGPT, generative artificial intelligence, students’ perceptions, secondary education
Review Article
Oksana K. Korobkova, Natalya A. Galchenko, Natalia A. Orekhovskaya, Ekaterina A. Drozdova, Oksana V. Zakharova, Alisa V. Lobuteva
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep611
ABSTRACT
This study is a thorough scoping review that seeks to thoroughly delineate the existing frameworks, difficulties, and prospective trajectories of educational technology for digital generations. Utilizing the technique established by Arksey and O’Malley (2005), a systematic search was conducted for English-language studies published between 2016 and 2025 in the WoS and Scopus databases, resulting in the inclusion of 46 publications in the analysis. The results show that technology acceptance models (TAM and UTAUT) (19 studies) are the most common in the field of educational technology. These models were established during the X-Y generation period and do not fully reflect the realities of digital generations. Digital learning infrastructures (n = 29) and mobile-social learning ecosystems (n = 22) were recognized as the predominant technological categories. The study revealed that the digital divide has intensified since the epidemic (28% lack Internet access), shortcomings in teacher preparedness constitute the principal obstacle to technological integration, and adaptive learning methods exhibit moderate to substantial impact sizes. The results underscore the necessity for the formulation of novel theoretical frameworks that encapsulate the inherent technology interaction patterns of digital generations, pedagogy-oriented technology integration, and holistic digital equity initiatives.
Keywords: educational technology, digital generation, Gen Z, Gen alpha, scoping review
Research Article
Gonzalo Camacho-Vásquez, Juana María Ortega-Tudela, Elena María Díaz-Pareja
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep612
ABSTRACT
Digital competency is becoming a priority in teachers’ professional development. This article assessed the efficacy of a web-based methodology on English teachers’ digital competence through a 200-hour course. This didactic methodology consists of eight learning moments that alternate between online and offline instruction and are based on solid theoretical foundations. The results of the six areas and 22 competencies of the DigCompEdu framework are compared between an entry and exit test in a group of 47 English teachers who were part of the web-based didactic design implementation. Data analysis was conducted using the statistical software R version 4.2.2. The Likert package version 1.3.5 and the ggplot2 package version 3.4.0 were used to generate graphs and descriptive statistics. The Stats package version 4.2.2 was used to perform the Kruskal-Wallis and Wilcoxon comparison tests. This analysis demonstrated that the effects of the designed methodology on teachers’ digital competence development are highly significant. Teacher-learners advanced in areas of the DigCompEdu related to students’ digital competence. The areas with fewer improvements were where teacher-learners scored higher in the pre-test. “Self-regulated learning,” “accessibility and inclusion,” and “communication and collaboration” competencies reached the highest difference when comparing the pre- and post-tests. Teachers struggle more with learning during offline moments. The Web-based methodology becomes an innovative proposal for training English teachers to incorporate technology to develop language skills.
Keywords: information and communication technology, English language teaching, teacher digital competence, teachers’ professional development
Review Article
Albinа R. Fayzullina, Alla A. Filippova, Natalya Y. Garnova, Dmitry V. Astakhov, Nadezhda Kalmazova, Zulfiya F. Zaripova
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep613
ABSTRACT
This systematic review investigates the incorporation of artificial intelligence (AI) into science education by analyzing 17 studies published from 2020 to 2024. The paper examines the utilization of AI in different scientific fields and educational settings and assesses its influence on the methods of teaching and learning. The findings demonstrate a diverse range of AI applications, including chatbots, intelligent tutoring systems, and AI-enhanced textbooks. These apps serve many functions, from being educational tools to assisting in assessments. The investigation demonstrates the favorable impact of AI on student performance, motivation, and engagement in science education, particularly in the areas of personalized learning and the development of self-regulated learning skills. Additionally, issues related to technological infrastructure, obstacles to the sensitivity and reliability of AI systems, and ethical issues were also examined. The study emphasizes the importance of teacher preparation in achieving the successful integration of AI and expresses the necessity of comprehensive professional development. Potential areas for future research encompass investigating the enduring consequences of AI utilization, exploring its applicability in diverse educational settings, and fostering the growth of AI literacy. The study’s findings indicate that while AI has the potential to greatly improve science education, its successful application necessitates thoughtful evaluation of technological, pedagogical, ethical, and social elements to ensure fair and efficient integration across all educational levels.
Keywords: artificial intelligence, science education, AI-enhanced teaching, educational chatbots
Research Article
Shaista Rashid
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep614
ABSTRACT
The study aims to explore the students’ perception of YouTube assisted language learning (YALL) in the English as a foreign language (EFL) context using the basic technology acceptance model (TAM) by Davies (1989). This study delves into YALL adoption using TAM model as it is an unexplored area and the present studies focuses on mere perception regarding YALL or the adoption of YouTube as an educational resource, but not on the adoption of YALL in EFL context. The study is based on a quantitative approach. The data was collected using a questionnaire adapted from Chau et al. (2024) through Google Forms. The sample of 272 students was collected by purposive sampling followed by random sampling. Data is subjected to analysis in Smart PLS 4.1.1.2 for PLS-SEM analysis. The findings reveal that reliability, convergent validity, and discriminant validity are valid in all perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI) constructs. Moreover, hypotheses testing results reveal that PEOU positively influences PU (b = 0.667, t = 11.600, p = 0.000). Furthermore, BI is also significantly impacted by PEOU (b = 0.362, t = 6.056, p = 0.000) and PU (b = 0.493, t = 8.634, p = 0.000). Lastly, the findings of the study are supported by the theory of planned behavior as the insights from TAM hint at the positive adoption, which implies that perceived behavioral control is promoting the adoption of YALL in university EFL context. This study offers practical insight to introduce YALL as a useful technique by educators to promote enhanced self-learning.
Keywords: EFL learners, language learning, TAM, YALL, theory of planned behavior
Research Article
Ya Mo, Eulho Jung, Meehyun Yoon
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep615
ABSTRACT
This study examines the effectiveness of a multi-level assessment protocol for online learning, particularly the use of reflection and self-assessment as learning and assessment tools, by capturing and interpreting five graduate students’ experiences in an online assessment course. Data were collected through surveys, reflection questions, self-assessments, pre-assessments, and module assessments. Likert-scale survey questions and selected-response reflection questions or assessment items were analyzed by counting responses or tallying correct answers. Open-ended survey questions, reflection questions, and assessment items were analyzed qualitatively using thematic analysis. Results reveal that combining traditional tests with reflection enhances assessments as learning tools, emphasizing the necessity of carefully formulated reflection questions. For self-assessment with constructed-response tests, providing students with illustrative responses and detailed rubrics that clearly state the number and scope of key points expected in responses can help standardize students’ subjective judgments. Furthermore, the unanimous preference for hands-on projects among students and the frustration expressed by some regarding asynchronous discussions underscore the importance of offering diverse, flexible, and engaging activities across various modalities to support learning outcomes. Overall, the findings affirm the enhancement of assessments as learning tools through the amalgamation of traditional tests with reflection and self-assessment, while also pinpointing the need for thoughtful implementation.
Keywords: online learning, assessment, reflection, self-assessment
Research Article
Rozalin Fareed Qazamel, Doa Omar Hawari
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep616
ABSTRACT
This study aims to explore the impact of distance learning on the quality of practical applications among early childhood education students in secondary schools within the Green Line. The descriptive correlational approach was used, utilizing a research tool consisting of two main axes: distance learning and the quality of practical applications. The validity and reliability of the tool were verified. The study sample consisted of teachers working with early childhood education students in secondary schools. A survey study was initially conducted by distributing the tool to a sample of 30 male and female teachers from the study population but outside the main research sample. After that, the final tool was distributed to the main sample, which included 160 male and female teachers who were selected through the comprehensive survey method. The data collected through an electronic questionnaire were statistically analyzed. The findings revealed a significant negative relationship between distance learning and the quality of practical applications, indicating that higher engagement in distance learning was associated with lower performance in practical skills and hands-on activities. This highlights the challenges of applying practical training effectively in a fully online learning environment.
Keywords: early childhood education, distance learning quality, practical applications
Research Article
Nadeyah J. Alreiahi, Noha Alrwaished
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep617
ABSTRACT
This study examined the participation of preservice teachers (PSTs) in artificial intelligence (AI)-generated lesson-plan training sessions, primarily within a mathematics instruction course in a teacher training program. Although earlier research has explored AI in education, few studies have specifically looked at how AI-generated lesson plans affect PSTs’ lesson-planning skills. This study addresses this gap by exploring math PSTs’ views on the use of AI-generated tools in lesson planning. Data were collected from focus group interviews with fifty students and were analyzed thematically using a coding framework after targeted training sessions. The findings revealed key themes, including the time-saving benefits of AI, its ability to produce innovative activities, and the support these tools provide to PSTs. While AI tools served as scaffolds, the importance of teacher agency, pedagogical knowledge, and content alignment remained vital. These results highlight the potential of AI support in teacher training programs, while also recognizing that it does not replace essential skills such as critical thinking and professional judgment. The study offers implications and recommendations for integrating AI while maintaining pedagogical rigor and curriculum integrity.
Keywords: artificial intelligence, preservice teachers, lesson planning, mathematics education, teacher training, pedagogical knowledge, higher education
Research Article
Gulnur Ussenova, Gulnara Issayeva, Ryskul Urazaliyeva, Galina Karimova, Marina Mukhanova
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep618
ABSTRACT
This study investigates the extent to which the use of digital educational resources (DER) predicts students’ cognitive independence (CI) in higher education and explores the moderating roles of psychological variables; motivation for digital learning, self-regulation skills (SRS), and cognitive engagement (CE). A total of 276 undergraduate students from Korkyt Ata Kyzylorda University in Kazakhstan participated in the study. Data were collected using a validated survey instrument covering five constructs: DER usage, motivation, self-regulation, CE, and CI. Moderation analyses were conducted using general linear models. The results revealed that DER usage is a significant positive predictor of CI. While motivation, self-regulation, and engagement were each strong direct predictors, only SRS and CE moderate the relationship between DER usage and CI. Specifically, students with lower levels of self-regulation or engagement benefited more from DER use. These findings show the compensatory role of digital tools in improving autonomy among less-prepared learners. The study contributes to the literature by identifying for whom DER is most effective and indicates the need for differentiated digital pedagogical strategies that promote independent learning.
Keywords: cognitive engagement, cognitive independence, digital educational resources, higher education, moderation analysis, motivation, self-regulation
Research Article
Liwei Hsu
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep619
ABSTRACT
This study examines the use of generative artificial intelligence, i.e., ChatGPT, in English as a foreign language (EFL) learning, emphasizing the mediating role of entangled cognition and the effects of the learning outcomes of the tourism students. The research was designed to a quasi-experiment which included 96 participants (48 in an experimental group and 48 in a control group) who were sampled based on convenience to the Spring 2024 semester in one university in southern Taiwan. The “custom virtual language course” experimental group used ChatGPT for personalized language practice and culture learning, control group received traditional learning. A questionnaire package, including the cognitive technology use questionnaire (CTUQ), extended mind scale (EMS), distributed cognition questionnaire (DCQ), metacognitive awareness inventory (MAI), and TOEIC pre- and post-tests was administered to collect the data. The difference-in-differences design was adopted and observed a significant treatment effect such that the treatment group had an average increase in mean scores of 37.98 (standard deviation [SD] = 7.80) compared to 19.62 (SD = 7.80) for the control group and, therefore, an average treatment effect of 21.38 (95% confidence interval [18.74, 24. 01]). Findings suggest that ChatGPT promotes cognitive offloading, distributed cognition, and metacognitive awareness (CTUQ mean [M] = 3.701, EMS M = 3.421, DCQ M = 3.721, MAI M = 3.551), and the development of collaborative learning and cultural competence. These results reveal ChatGPT’s potential to reform EFL education, but they also indicate the necessity to mitigate the risks associated with ethical quandaries and over-dependence. Future studies need to create specific scales that can be used for entangled cognition and examine the long-term effects on cognition.
Keywords: ChatGPT, difference-in-differences approach, EFL education, entangled cognition, generative artificial intelligence