AI Trends in Teacher and Student Training

Topic Information

Dear Colleagues,

Artificial intelligence is transforming education at all levels, from early childhood education to university. This Topic seeks to explore the latest trends and developments in the application of AI in education for teachers and students, showcasing case studies, innovative practices and emerging technologies. A variety of topics related to AI in teacher and student education will be addressed, including, but not limited to, AI-powered teaching tools, student data analysis, research advances, and the ethical implications of AI integration. In this regard, it is important to uncover both positive and negative consequences on society. By examining how AI is reshaping academic environments, this theme seeks to contribute to a deeper understanding of how educational institutions can leverage AI to improve educational outcomes and operational efficiency.

Potential topics include, but are not limited to, the following:

• AI-powered personalized learning platforms;
• Use of AI in the analysis and prediction of student performance;
• AI-driven research innovations in various academic areas;
• Ethical concerns and challenges in implementing AI in education;
• AI administrative tools to improve the management and efficiency of educational institutions;
• The role of AI in fostering accessibility and inclusion in education;
• The impact of AI on the future of teaching and learning methodologies across different educational levels;
• Case studies on the successful integration of AI in educational environments.

Dr. José Fernández-Cerero
Dr. Marta Montenegro-Rueda
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	5.0	6.9	2020	19.2 Days	CHF 1800	Submit
Computers computers	4.2	7.5	2012	17.5 Days	CHF 1800	Submit
Education Sciences education	2.6	5.5	2011	26.5 Days	CHF 1800	Submit
Societies societies	1.6	3.0	2011	29.9 Days	CHF 1600	Submit
Future Internet futureinternet	3.6	8.3	2009	16.1 Days	CHF 1800	Submit
Technologies technologies	3.6	8.5	2013	19.1 Days	CHF 1800	Submit

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Published Papers (19 papers)

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20 pages, 820 KB  

Open AccessArticle

Triadic Instructional Design: The Impact of Structured AI Training on Pre-Service Teachers’ Intelligent-TPACK, Attitudes, and Lesson Planning Skills

by Shan Jiang and Jinzhen Li

Educ. Sci. 2026, 16(2), 315; https://doi.org/10.3390/educsci16020315 (registering DOI) - 14 Feb 2026

Abstract

Artificial Intelligence (AI) holds transformative potential to revolutionize teaching and learning, yet its rapid integration poses significant challenges for teacher preparation. While AI competencies—encompassing knowledge, skills, and attitudes—are critical for effective integration, limited research has holistically addressed these three interconnected domains. To bridge [...] Read more.

Artificial Intelligence (AI) holds transformative potential to revolutionize teaching and learning, yet its rapid integration poses significant challenges for teacher preparation. While AI competencies—encompassing knowledge, skills, and attitudes—are critical for effective integration, limited research has holistically addressed these three interconnected domains. To bridge this gap, this quasi-experimental study (N = 259) evaluated a triadic instructional design synergizing the intelligent technological, pedagogical, and content knowledge (Intelligent-TPACK) framework, Synthesis of Qualitative Data model, and curated AI tools. Pre-service English as a foreign language (EFL) teachers were assigned to an experimental group (n = 137) receiving the structured intervention or a control group (n = 122) engaging in self-directed AI exploration. Results reveal that the experimental group achieved greater gains across all Intelligent-TPACK dimensions and demonstrated higher-order AI applications in lesson planning. Furthermore, the experimental group experienced a significant reduction in perceived pressure and reported higher perceived usefulness regarding AI integration. Qualitative data revealed that hands-on AI tasks enhanced participants’ confidence, yet challenges with prompts and critical adaptation persisted. The findings demonstrate that systematic training is essential for transforming pre-service teachers’ passive awareness into competent AI integration. Finally, this paper proposes practical implications for integrating this triadic framework into teacher education curricula to facilitate sustainable AI adoption. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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11 pages, 201 KB  

Open AccessArticle

Educational Potential of Artistic Mediation with Children at Risk of Exclusion Through Teachers’ Narratives

by María Dolores López-Martínez, Margarita Campillo Díaz and Amalia Ayala de la Peña

Societies 2026, 16(2), 44; https://doi.org/10.3390/soc16020044 - 30 Jan 2026

Viewed by 247

Abstract

The research explores the educational potential of artistic mediation with children at risk of social exclusion, drawing on the narratives of twenty early years and primary school teachers. Using a qualitative, phenomenological approach, it examines perceptions of openness to the creative process, the [...] Read more.

The research explores the educational potential of artistic mediation with children at risk of social exclusion, drawing on the narratives of twenty early years and primary school teachers. Using a qualitative, phenomenological approach, it examines perceptions of openness to the creative process, the use of art in teaching practice and its value as a socio-educational tool. The findings show that experiences of artistic mediation generate feelings of harmony, concentration and achievement, thus fostering a more collaborative and emotionally balanced classroom climate. The study also observes that art serves as a means for teachers’ reflective practice, encouraging critical thinking, the formulation of questions and an approach to assessment that focuses more on processes than products. In vulnerable contexts, artistic mediation proves particularly effective for expressing emotions, strengthening self-esteem and reinforcing group cohesion. Taken together, the findings suggest that artistic mediation should be understood beyond its instrumental value, recognising it as a transformative practice that promotes both educational inclusion and professional reflection on teaching, thereby helping to enhance the quality and humanistic purpose of pedagogical interventions. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

19 pages, 480 KB  

Open AccessArticle

Acceptance and Use of Generative Artificial Intelligence in Higher Education: A UTAUT-Based Model Integrating Trust and Privacy

by Lidija Weis, Julija Lapuh Bele and Vanja Erčulj

Educ. Sci. 2026, 16(2), 173; https://doi.org/10.3390/educsci16020173 - 23 Jan 2026

Viewed by 479

Abstract

The rapid emergence of generative artificial intelligence (GAI) is reshaping academic work in higher education. While classical technology acceptance models primarily emphasize cognitive and instrumental determinants, the adoption of GAI also raises ethical concerns related to trust in AI systems and the protection [...] Read more.

The rapid emergence of generative artificial intelligence (GAI) is reshaping academic work in higher education. While classical technology acceptance models primarily emphasize cognitive and instrumental determinants, the adoption of GAI also raises ethical concerns related to trust in AI systems and the protection of personal and institutional data. To address this gap, this study examines the determinants of GAI acceptance and use among academic staff in Slovenian higher education institutions by applying a UTAUT-based model that integrates trust and privacy. In this study, GAI is conceptualized as a class of text-based generative AI tools commonly used in academic practice, including applications such as ChatGPT, Copilot, Scholar AI, Gemini, Consensus, and similar systems. A quantitative research design was employed, based on a structured online survey administered to academic staff across 20 higher education institutions in Slovenia (n = 201). Data were analyzed using multilevel confirmatory factor analysis and generalized estimating equations. The results indicate that performance expectancy and attitude toward using significantly predict behavioral intention to use GAI (B = 0.49, p < 0.001 for both), while behavioral intention is the primary predictor of actual use behavior (B = 0.93, p < 0.001). Effort expectancy is positively associated with use behavior independent of behavioral intention (B = 0.23, p = 0.012), whereas trust does not show a statistically significant association with use behavior (B = 0.05, p = 0.458) or behavioral intention (B = −0.01, p = 0.840). Privacy exhibits a positive, but non-statistically significant, association with use behavior (B = 0.12, p = 0.058). The findings highlight the relevance of considering both cognitive and ethical factors when examining generative AI adoption in academic contexts and provide initial empirical insights for refining UTAUT-based frameworks in the context of emerging AI technologies. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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21 pages, 2948 KB  

Open AccessArticle

Teacher Professional Development: A Workshop Proposal for High School–University Collaboration Using Technology and AI

by Guillermina Ávila García, Liliana Suárez Téllez, Mario Humberto Ramírez Díaz and Francisco Antonio Horta Rangel

Educ. Sci. 2026, 16(1), 153; https://doi.org/10.3390/educsci16010153 - 19 Jan 2026

Viewed by 597

Abstract

This study explores the integration of technology and artificial intelligence (AI) as catalysts for professional teacher development within the context of Mexico’s educational challenges. Adopting a qualitative and exploratory approach, a four-phase workshop was conducted with 40 high school and university-level teachers from [...] Read more.

This study explores the integration of technology and artificial intelligence (AI) as catalysts for professional teacher development within the context of Mexico’s educational challenges. Adopting a qualitative and exploratory approach, a four-phase workshop was conducted with 40 high school and university-level teachers from the National Polytechnic Institute (IPN). The methodology included scientific modeling activities using traditional methods, software (Tracker, ver. 6.2.0), and AI tools (ChatGPT-3.5), while analyzing participants’ perceptions and experiences. The findings reveal a clear disconnect between teachers’ theoretical competencies and their practical skills, with persistent gaps in scientific literacy at both educational levels. However, this study documents that the workshop functioned as a genuine professional learning community, where inter-academic collaboration and peer-learning proved to be an effective strategy for addressing these deficiencies. Technology, specifically the Tracker software, served as a catalyst for conceptual understanding. Despite AI’s potential for research, its limitations in the precision of responses reinforced this study’s central conclusion: technology does not replace the teacher’s work but transforms the teacher’s role into a critical mediator, responsible for guiding students to develop analytical and critical thinking in a complex digital environment. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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16 pages, 529 KB  

Open AccessReview

Conceptualizing the Impact of AI on Teacher Knowledge and Expertise: A Cognitive Load Perspective

by Irfan Ahmed Rind

Educ. Sci. 2026, 16(1), 57; https://doi.org/10.3390/educsci16010057 - 1 Jan 2026

Viewed by 1073

Abstract

Artificial intelligence (AI) is increasingly embedded in education through adaptive platforms, intelligent tutoring systems, and generative tools. While these technologies promise efficiency and personalization, they also raise concerns about pedagogical deskilling, reduced teacher autonomy, and ethical risks. This paper conceptualizes the potential impacts [...] Read more.

Artificial intelligence (AI) is increasingly embedded in education through adaptive platforms, intelligent tutoring systems, and generative tools. While these technologies promise efficiency and personalization, they also raise concerns about pedagogical deskilling, reduced teacher autonomy, and ethical risks. This paper conceptualizes the potential impacts of AI on teaching expertise and instructional design through the lens of Cognitive Load Theory (CLT). The aim is to conceptualize how AI may reshape the management of intrinsic, extraneous, and germane cognitive loads. The study proposes that AI may effectively scaffold intrinsic load and reduce extraneous distractions but displace teacher judgment in ways that undermine germane learning and reflective practice. Additionally, opacity, algorithmic bias, and inequities in access may create new forms of cognitive and ethical burden. The conceptualization presented in this paper contributes to scholarship by foregrounding teacher cognition, an underexplored dimension of AI research, conceptualizing the teacher as a cognitive orchestrator who balances human and algorithmic inputs, and integrating ethical and equity considerations into a cognitive framework. Recommendations are provided for teacher education, policy, and AI design, emphasizing the need for pedagogy-driven integration that preserves teacher expertise and supports deep learning. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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26 pages, 1381 KB  

Open AccessArticle

Integrating Generative AI into Live Case Studies for Experiential Learning in Operations Management

by David Ernesto Salinas-Navarro, Eliseo Vilalta-Perdomo, Jaime Alberto Palma-Mendoza and Martina Carlos-Arroyo

Educ. Sci. 2026, 16(1), 15; https://doi.org/10.3390/educsci16010015 - 23 Dec 2025

Viewed by 630

Abstract

This research-to-practice study examines how Generative Artificial Intelligence (GenAI) can be integrated into live case studies to enhance experiential learning in higher education. It explores GenAI’s potential as an agent to learn with scaffolding reflection and engagement and addresses gaps in existing applications [...] Read more.

This research-to-practice study examines how Generative Artificial Intelligence (GenAI) can be integrated into live case studies to enhance experiential learning in higher education. It explores GenAI’s potential as an agent to learn with scaffolding reflection and engagement and addresses gaps in existing applications that often focus narrowly on content generation. To explore GenAI’s agentive potential, the methodology illustrates this approach in a UK postgraduate operations management module. Students engaged in a live case study of a local ethnic restaurant to refine its business model and operations. The data sources used to examine students’ results included module materials, outputs, and feedback surveys. Thematic analysis was employed to assess how GenAI facilitated experiential learning. The findings suggest that GenAI integration facilitated exploration, reflection, conceptualisation, and experimentation. Students reported that the activity was engaging and relevant, facilitating critical decision-making and understanding of operations management. However, the outcomes varied according to GenAI literacy and student participation. Although GenAI-enriched learning is beneficial, human agency and contextual knowledge remain crucial. Overall, this study integrates GenAI as a cognitive partner throughout Kolb’s ELC. This study offers a transferable framework for active learning, illustrating how technology can enhance critical and reflective learning in authentic educational contexts. However, limitations include uneven student participation and engagement, resource constraints, overreliance on artificial intelligence outputs, differentiated impact on learning outcomes, and a single-case report, which must be addressed before the framework can be scaled up. Future research should test this through multi-case studies while developing GenAI literacy, measuring GenAI impact, and implementing ethical practices in the field. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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20 pages, 528 KB  

Open AccessArticle

Learning with Generative AI: An Empirical Study of Students in Higher Education

by Golan Carmi

Educ. Sci. 2025, 15(12), 1696; https://doi.org/10.3390/educsci15121696 - 16 Dec 2025

Viewed by 2096

Abstract

Generative AI technologies are rapidly permeating higher education as innovative tools that support teaching and learning processes. This study investigates the integration of GenAI tools into academic learning and examines their influence on students’ learning effectiveness, attitudes, and satisfaction. A quantitative survey was [...] Read more.

Generative AI technologies are rapidly permeating higher education as innovative tools that support teaching and learning processes. This study investigates the integration of GenAI tools into academic learning and examines their influence on students’ learning effectiveness, attitudes, and satisfaction. A quantitative survey was administered to 485 college students. The findings indicate that students’ attitudes, satisfaction, and accumulated experience with GenAI constitute the most influential factors in promoting effective learning. Perceived advantages and disadvantages also play a substantial role in shaping students’ attitudes, satisfaction, and learning outcomes. Ethical knowledge demonstrates only modest positive effects, whereas institutional training shows no meaningful impact, largely due to its limited availability. The results suggest that higher education institutions should not focus solely on tool accessibility and technical training, but should prioritize fostering positive perceptions, maximizing the perceived benefits of GenAI, offering applied instruction and practical ethical guidance, and reducing concerns and negative perceptions among students. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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16 pages, 354 KB  

Open AccessArticle

AI-Based Intelligent System for Personalized Examination Scheduling

by Marco Barone, Muddasar Naeem, Matteo Ciaschi, Giancarlo Tretola and Antonio Coronato

Technologies 2025, 13(11), 518; https://doi.org/10.3390/technologies13110518 - 12 Nov 2025

Viewed by 979

Abstract

Artificial Intelligence (AI) has brought a revolution in many areas, including the education sector. It has the potential to improve learning practices, innovate teaching, and accelerate the path towards personalized learning. This work introduces Reinforcement Learning (RL) methods to develop a personalized examination [...] Read more.

Artificial Intelligence (AI) has brought a revolution in many areas, including the education sector. It has the potential to improve learning practices, innovate teaching, and accelerate the path towards personalized learning. This work introduces Reinforcement Learning (RL) methods to develop a personalized examination scheduling system at a university level. We use two widely established RL algorithms, Q-Learning and Proximal Policy Optimization (PPO), for the task of personalized exam scheduling. We consider several key points, including learning efficiency, the quality of the personalized educational path, adaptability to changes in student performance, scalability with increasing numbers of students and courses, and implementation complexity. Experimental results, based on case studies conducted within a single degree program at a university, demonstrate that, while Q-Learning offers simplicity and greater interpretability, PPO offers superior performance in handling the complex and stochastic nature of students’ learning trajectories. Experimental results, conducted on a dataset of 391 students and 5700 exam records from a single degree program, demonstrate that PPO achieved a 42.0% success rate in improving student scheduling compared to Q-Learning’s 26.3%, with particularly strong performance on problematic students (41.3% vs 18.0% improvement rate). The average delay reduction was 5.5 months per student with PPO versus 3.0 months with Q-Learning, highlighting the critical role of algorithmic design in shaping educational outcomes. This work contributes to the growing field of AI-based instructional support systems and offers practical guidance for the implementation of intelligent tutoring systems. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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18 pages, 1332 KB  

Open AccessArticle

Balancing Affective Engagement and Cognitive Load in Generative-AI-Based Learning: Empathy, Immersion, and Emotional Design in Design Education

by Wonsub Lee, Sungbok Chang and Jungho Suh

Educ. Sci. 2025, 15(11), 1478; https://doi.org/10.3390/educsci15111478 - 3 Nov 2025

Viewed by 1681

Abstract

As higher education undergoes rapid transformation driven by Artificial Intelligence (AI), the integration of Generative AI (GenAI) has become essential for preparing future-ready creative professionals. In this context, design education plays a leading role in exploring how GenAI can enhance students’ experiential learning. [...] Read more.

As higher education undergoes rapid transformation driven by Artificial Intelligence (AI), the integration of Generative AI (GenAI) has become essential for preparing future-ready creative professionals. In this context, design education plays a leading role in exploring how GenAI can enhance students’ experiential learning. This study empirically examined how three experience dimensions—Educational, Entertainment, and Aesthetic—shape Empathy, Immersion, Satisfaction, and Learning Outcomes in a GenAI-based self-character workshop. A total of 185 design students participated, and the data were analyzed using Structural Equation Modeling (SEM). The results revealed that both Entertainment (β = 0.334, p < 0.001) and Aesthetic (β = 0.434, p < 0.001) experiences significantly and positively predicted Empathy and also increased Immersion (β = 0.215, p < 0.001; β = 0.154, p < 0.05). In contrast, Educational experience showed a non-significant or slightly negative effect. Furthermore, Empathy enhanced Immersion (β = 0.220, p < 0.01), Satisfaction (β = 0.173, p < 0.05), and Learning Outcomes (β = 0.305, p < 0.001). Immersion also improved Learning Outcomes (β = 0.253, p < 0.05) but slightly reduced short-term Satisfaction (β = −0.186, p < 0.05), indicating a cognitive-load trade-off between concentration and immediate enjoyment. These findings demonstrate that GenAI-based creative activities can effectively foster both emotional engagement and learning performance when instructional design minimizes unnecessary cognitive burden. The study contributes to understanding how emotionally meaningful and aesthetically engaging experiences can advance AI-integrated design education in the digital transformation era. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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38 pages, 2629 KB  

Open AccessArticle

Exploring the Use of AI to Optimize the Evaluation of a Faculty Training Program

by Alexandra Míguez-Souto, María Ángeles Gutiérrez García and José Luis Martín-Núñez

Educ. Sci. 2025, 15(10), 1394; https://doi.org/10.3390/educsci15101394 - 17 Oct 2025

Viewed by 1217

Abstract

This study examines the potential of the AI chatbot ChatGPT-4o to support human-centered tasks such as qualitative research analysis. It focuses on a case study involving an initial university teaching training program at the Universidad Politécnica de Madrid (UPM), evaluated through student feedback. [...] Read more.

This study examines the potential of the AI chatbot ChatGPT-4o to support human-centered tasks such as qualitative research analysis. It focuses on a case study involving an initial university teaching training program at the Universidad Politécnica de Madrid (UPM), evaluated through student feedback. The findings indicate that ChatGPT can assist in the qualitative analysis of student assessments by identifying specific issues and suggesting possible solutions. However, expert oversight remains necessary as the tool lacks a full contextual understanding of the actions evaluated. The study concludes that AI systems like ChatGPT offer powerful means to complement complex human-centered tasks and anticipates their growing role in the evaluation of formative programs. By examining ChatGPT’s performance in this context, the study lays the groundwork for prototyping a customized automated system built on the insights gained here, capable of assessing program outcomes and supporting iterative improvements throughout each module, with the ultimate goal of enhancing the quality of the training program Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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24 pages, 638 KB  

Open AccessArticle

Determinants of Chatbot Brand Trust in the Adoption of Generative Artificial Intelligence in Higher Education

by Oluwanife Segun Falebita, Joshua Abah Abah, Akorede Ayoola Asanre, Taiwo Oluwadayo Abiodun, Musa Adekunle Ayanwale and Olubunmi Kayode Ayanwoye

Educ. Sci. 2025, 15(10), 1389; https://doi.org/10.3390/educsci15101389 - 17 Oct 2025

Cited by 1 | Viewed by 1808

Abstract

The use of generative artificial intelligence (GenAI) chatbots in brands is growing exponentially, and higher education institutions are not unaware of how such tools effectively shape the attitudes and behavioral intentions of students. These chatbots are able to synthesize an enormous amount of [...] Read more.

The use of generative artificial intelligence (GenAI) chatbots in brands is growing exponentially, and higher education institutions are not unaware of how such tools effectively shape the attitudes and behavioral intentions of students. These chatbots are able to synthesize an enormous amount of data input and can create contextually aware, human-like conversational content that is not limited to simple scripted responses. This study examines the factors that determine chatbot brand trust in the adoption of GenAI in higher education. By extending the Technology Acceptance Model (TAM) with the construct of brand trust, the study introduces a novel contribution to the literature, offering fresh insights into how trust in GenAI chatbots is developed within the academic context. Using the convenience sampling technique, a sample of 609 students from public universities in North Central and Southwestern Nigeria was selected. The collected data were analyzed via partial least squares structural equation modelling. The results indicated that attitudes toward chatbots determine behavioral intentions and GenAI chatbot brand trust. Surprisingly, behavioral intentions do not affect GenAI chatbot brand trust. Similarly, the perceived ease of use of chatbots does not determine behavioral intention or attitudes toward GenAI chatbot adoption but rather determines perceived usefulness. Additionally, the perceived usefulness of chatbots affects behavioral intention and attitudes toward GenAI chatbot adoption. Moreover, social influence affects behavioral intention, perceived ease of use, perceived usefulness and attitudes toward GenAI chatbot adoption. The implications of the findings for higher education institutions are that homegrown GenAI chatbots that align with the principles of the institution should be developed, creating an environment that promotes a positive attitude toward these technologies. Specifically, the study recommends that policymakers and university administrators establish clear institutional guidelines for the design, deployment, and ethical use of homegrown GenAI chatbots, ensuring alignment with educational goals and safeguarding student trust. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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16 pages, 1523 KB  

Open AccessArticle

The Effects of a Teacher Training Program on Students’ Perceptions of the Teaching–Learning Process

by Jorge López González, Belén Obispo-Díaz and Jesús Rodríguez Barroso

Societies 2025, 15(10), 272; https://doi.org/10.3390/soc15100272 - 28 Sep 2025

Viewed by 890

Abstract

The aim of this article is to identify the effectiveness of a teacher education program based on student perceptions. In this aim, a longitudinal research project was carried out with a sample of 14,229 students at a Spanish university who evaluated their teachers [...] Read more.

The aim of this article is to identify the effectiveness of a teacher education program based on student perceptions. In this aim, a longitudinal research project was carried out with a sample of 14,229 students at a Spanish university who evaluated their teachers (using a Likert-type scale) after they completed a teacher training program. The CEDA teacher evaluation scale (α = 0.968; ω = 0.968) was used to assess students’ perceptions of the instructor’s role as a facilitator of learning. Complementary qualitative information was also collected, which complemented the quantitative findings. The first conclusion is the positive impact of key variables of the teacher training program: the pedagogical model, educational innovation, and evaluation strategies. Secondly, the students’ perception was slightly better in relation to the pedagogical model, followed by evaluation strategies and finally educational innovation. Thirdly, although students generally rated the teaching of technical subjects more highly than the humanities, the perception of change linked to teacher training was positive for all subjects. Finally, there was a slight difference in students’ perceptions according to the academic course (second, third, or fourth). All of the above should be considered for future teacher training programs. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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24 pages, 5485 KB  

Open AccessArticle

SQUbot: Enhancing Student Support Through a Personalized Chatbot System

by Zia Nadir, Hassan M. Al Lawati, Rayees A. Mohammed, Muna Al Subhi and Abdulnasir Hossen

Technologies 2025, 13(9), 416; https://doi.org/10.3390/technologies13090416 - 15 Sep 2025

Viewed by 1679

Abstract

Educational institutions commonly receive numerous student requests regarding various services. Given the large population of students in a college, it becomes extremely overwhelming for the staff to address the inquiries of all the students while dealing with multiple administrative tasks at the same [...] Read more.

Educational institutions commonly receive numerous student requests regarding various services. Given the large population of students in a college, it becomes extremely overwhelming for the staff to address the inquiries of all the students while dealing with multiple administrative tasks at the same time. Furthermore, students often make multiple visits to the university’s administration, make multiple calls, or write emails about their concerns, which makes it difficult to respond to their queries promptly. AI-powered chatbots can act as virtual assistants that promptly help students in addressing their simple and complex queries. Most of the research work has focused on chatbots supporting the English language, and significant improvement is needed for implementing chatbots in the Arabic language. Existing studies supporting the Arabic language have either employed rule-based models or built custom deep learning models for chatbots. Rule-based models lack understanding of diverse contexts, whereas custom-built deep learning models, besides needing huge datasets for effective training, are difficult to integrate with other platforms. In this work, we leverage the services offered by IBM Watson to develop a chatbot that assists university students in both English and Arabic. IBM Watson employs natural language understanding and deep learning techniques to build a robust dialog and offers a more scalable, integrable, and customizable solution for enterprises. The chatbot not only provides information about the university’s general services but also customizes its response based on the individual needs of the students. The chatbot has been deployed at Sultan Qaboos University (SQU), Oman, and tested by the university’s staff and students. User testing shows that the chatbot achieves promising results. This first bilingual AI chatbot at SQU supports English and Arabic and offers secure, personalized services via OTP and student email verification. SQUbot delivers both general and individualized academic support. Pilot testing showed 84.9% intent recognition accuracy. Most unidentified queries were due to dialectal variation or out-of-scope inputs, which were addressed through fallback prompts and dataset refinement. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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18 pages, 778 KB  

Open AccessArticle

From Theoretical Navigation to Intelligent Prevention: Constructing a Full-Cycle AI Ethics Education System in Higher Education

by Xingjian Xu, Fanjun Meng and Yan Gou

Educ. Sci. 2025, 15(9), 1199; https://doi.org/10.3390/educsci15091199 - 11 Sep 2025

Viewed by 2580

Abstract

The rapid integration of artificial intelligence (AI), particularly generative AI (Gen-AI), into higher education presents a critical challenge: preparing students for the complex ethical dilemmas inherent in AI-driven research and practice. Current AI ethics education, however, often remains fragmented, overly theoretical, and disconnected [...] Read more.

The rapid integration of artificial intelligence (AI), particularly generative AI (Gen-AI), into higher education presents a critical challenge: preparing students for the complex ethical dilemmas inherent in AI-driven research and practice. Current AI ethics education, however, often remains fragmented, overly theoretical, and disconnected from practical application, leaving a significant gap between knowing ethical principles and acting upon them. To address this pressing issue, this study proposes and validates a full-cycle AI ethics education system designed to bridge this gap. The system integrates three core components: (1) an updated four-dimensional ethics framework focused on Gen-AI challenges (research review, data privacy, algorithmic fairness, intellectual property); (2) a “cognition-behavior” dual-loop training mechanism that combines theoretical learning with hands-on, simulated practice; and (3) a full life-cycle education platform featuring tools like virtual laboratories to support experiential learning. A mixed-methods study with 360 students and 20 instructors demonstrated the system’s effectiveness, showing significant improvement in students’ ethical knowledge, a large effect size in enhancing ethical decision-making capabilities, and high user satisfaction. These findings validate a scalable model for AI ethics education that moves beyond passive instruction toward active, situated learning, offering a robust solution for higher education institutions to cultivate ethical responsibility in the age of Gen-AI. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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20 pages, 369 KB  

Open AccessArticle

Exploring University Students’ Acceptance and Satisfaction of the Flipped Learning Approach in Instructional Technology Related Class

by Asma’a Abu Qbeita and Al-Mothana Gasaymeh

Educ. Sci. 2025, 15(9), 1181; https://doi.org/10.3390/educsci15091181 - 8 Sep 2025

Cited by 1 | Viewed by 2205

Abstract

There is increasing interest in integrating various forms of Information and Communication technologies (ICT) into education. Well-established theoretical guidelines should guide the integration of these technologies. A flipped classroom is an example of an educational approach that integrates ICT and is guided by [...] Read more.

There is increasing interest in integrating various forms of Information and Communication technologies (ICT) into education. Well-established theoretical guidelines should guide the integration of these technologies. A flipped classroom is an example of an educational approach that integrates ICT and is guided by an active learning philosophy. The current study aims to evaluate participants’ acceptance of the flipped learning instructional model using six indicators—perceived usefulness, ease of use, hedonic motivation, attitude, self-efficacy, and educational quality—and to assess overall satisfaction. Additionally, it examines how these factors relate to overall satisfaction with this approach. The study utilized a descriptive cross-sectional research design with an exploratory and correlational orientation. The target population for this study included undergraduate students enrolled in the “Computer Applications in Education” course offered by the College of Education over three consecutive semesters: the second semester of the 2023/2024 academic year and the first and second semesters of 2024/2025. All students in this course experienced the flipped learning model as part of their instructional activities. Out of the 180 students, 137 completed the data collection tool, which was a questionnaire. The results showed that participants’ acceptance of the flipped learning approach was generally positive, ranging from moderate to high across all measured dimensions. The majority reported high levels of hedonic motivation, positive attitudes, perceived educational quality, and ease of use of the flipped learning requirements. Students found the flipped learning experience enjoyable, effective, and manageable. They believed it enhanced their learning and reported moderate self-efficacy and perceived usefulness. While satisfaction with flipped learning was moderate overall, it was strongly associated with enjoyment, positive attitudes, self-efficacy, and perceived educational quality, as evident in the results of the correlation analysis. Regression analysis revealed that these four factors were significantly associated with students’ satisfaction, whereas perceived usefulness and ease of use were not significantly associated when considered alongside other variables. These results suggest that emotional engagement, confidence, and perceived educational value are key contributors to students’ satisfaction with flipped learning. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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20 pages, 510 KB  

Open AccessArticle

Students’ Perceptions of Generative AI Image Tools in Design Education: Insights from Architectural Education

by Michelle Boyoung Huh, Marjan Miri and Torrey Tracy

Educ. Sci. 2025, 15(9), 1160; https://doi.org/10.3390/educsci15091160 - 5 Sep 2025

Cited by 4 | Viewed by 3253

Abstract

The rapid emergence of generative artificial intelligence (GenAI) has sparked growing interest across educational disciplines, reshaping how knowledge is produced, represented, and assessed. While recent research has increasingly explored the implications of text-based tools such as ChatGPT in education, far less attention has [...] Read more.

The rapid emergence of generative artificial intelligence (GenAI) has sparked growing interest across educational disciplines, reshaping how knowledge is produced, represented, and assessed. While recent research has increasingly explored the implications of text-based tools such as ChatGPT in education, far less attention has been paid to image-based GenAI tools—despite their particular relevance to fields grounded in visual communication and creative exploration, such as architecture and design. These disciplines raise distinct pedagogical and ethical questions, given their reliance on iteration, authorship, and visual representation as core elements of learning and practice. This exploratory study investigates how architecture and interior architecture students perceive the use of AI-generated images, focusing on ethical responsibility, educational relevance, and career implications. To ensure participants had sufficient exposure to visual GenAI tools, we conducted a series of workshops before surveying 42 students familiar with image generation processes. Findings indicate strong enthusiasm for GenAI image tools, which students viewed as supportive during early-stage design processes and beneficial to their creativity and potential future professional competitiveness. Participants regarded AI use as ethically acceptable when accompanied by transparent acknowledgment. However, acceptance declined in later design stages, where originality and critical judgment were perceived as more central. While limited in scope, this exploratory study foregrounds student voices to offer preliminary insights into evolving conversations about AI in creative education and to inform future reflection on developing ethically and pedagogically responsive curricula across the design disciplines. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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19 pages, 433 KB  

Open AccessArticle

A TAM-Based Analysis of Hong Kong Undergraduate Students’ Attitudes Toward Generative AI in Higher Education and Employment

by Kam Cheong Li, Grace Ho Lan Chong, Billy Tak Ming Wong and Manfred Man Fat Wu

Educ. Sci. 2025, 15(7), 798; https://doi.org/10.3390/educsci15070798 - 20 Jun 2025

Cited by 3 | Viewed by 4857

Abstract

This study explores undergraduate students’ attitudes towards generative AI tools in higher education and their perspectives on the future of jobs. It aims to understand the decision-making processes behind adopting these emerging technologies. A multidimensional model based on the technology acceptance model was [...] Read more.

This study explores undergraduate students’ attitudes towards generative AI tools in higher education and their perspectives on the future of jobs. It aims to understand the decision-making processes behind adopting these emerging technologies. A multidimensional model based on the technology acceptance model was developed to assess various factors, including perceived ease of use, perceived benefits, perceived concerns, knowledge of AI, and students’ perceptions of generative AI’s impact on the future of jobs. Data were collected through a survey distributed to 93 undergraduate students at a university in Hong Kong. The findings of multiple regression analyses revealed that these factors collectively explained 23% of the variance in frequency of use [(F(4, 78) = 5.89, p < 0.001), R² = 0.23]. Perceived benefits played the most significant role in determining frequency of use of generative AI tools. While students expressed mixed attitudes toward the role of AI in the future of jobs, those who voiced concerns about AI in education were more likely to view generative AI as a potential threat to job availability. The results provide insights for educators and policymakers to promote the effective use of generative AI tools in academic settings to help mitigate risks associated with overreliance, biases, and the underdevelopment of essential soft skills, including critical thinking, creativity, and communication. By addressing these challenges, higher education institutions can better prepare students for a rapidly evolving, AI-driven workforce. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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14 pages, 204 KB  

Open AccessArticle

Perceptions of AI in Higher Education: Insights from Students at a Top-Tier Chinese University

by Yi Yan, Bin Wu, Jiaqi Pi and Xiaowen Zhang

Educ. Sci. 2025, 15(6), 735; https://doi.org/10.3390/educsci15060735 - 12 Jun 2025

Cited by 4 | Viewed by 7943

Abstract

While AI integration in higher education has transformative potential, existing studies may not fully capture the unique socio-cultural and institutional contexts of top-tier universities in China. This study investigates students’ perceptions of AI utilization at a leading Chinese university, drawing on the Technology [...] Read more.

While AI integration in higher education has transformative potential, existing studies may not fully capture the unique socio-cultural and institutional contexts of top-tier universities in China. This study investigates students’ perceptions of AI utilization at a leading Chinese university, drawing on the Technology Acceptance Model (TAM). Quantitative data were collected via a 5-point Likert scale questionnaire (n = 253), complemented by open-ended qualitative responses. Results revealed that while they viewed AI as useful for enhancing efficiency and easy to use, concerns about content accuracy, over-reliance, and ethical issues persisted. Their high interest in AI contrasted with lower self-assessed proficiency, highlighting a gap between enthusiasm and competence. Institutional support significantly motivated adoption, whereas social influence played a lesser role. Students valued AI’s support in language learning, writing, research, and programming but noted its limitations in complex problem-solving. They also called for human-centric AI tools offering emotional support and personalized guidance. These findings may offer educators, policymakers, and AI developers valuable insights to address students’ concerns and optimize learning experiences in competitive academic environments. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

22 pages, 699 KB  

Open AccessArticle

Integration of Artificial Intelligence in K-12: Analysis of a Three-Year Pilot Study

by Boško Lišnić, Goran Zaharija and Saša Mladenović

AI 2025, 6(3), 49; https://doi.org/10.3390/ai6030049 - 1 Mar 2025

Cited by 2 | Viewed by 4438

Abstract

A three-year pilot study investigated the effectiveness of artificial intelligence (AI) as a motivational tool for teaching programming concepts within the Croatian Informatics curriculum. The study was conducted in schools through the extracurricular activity EDIT CodeSchool with the Development of Intelligent Web Applications [...] Read more.

A three-year pilot study investigated the effectiveness of artificial intelligence (AI) as a motivational tool for teaching programming concepts within the Croatian Informatics curriculum. The study was conducted in schools through the extracurricular activity EDIT CodeSchool with the Development of Intelligent Web Applications (RIWA) module. Twelve schools in Split-Dalmatia County in the Republic of Croatia participated, resulting in 112 successfully completed student projects. The program consisted of two phases: (1) theoretical instruction with examples and exercises, and (2) project-based learning, where students developed final projects using JavaScript and the ml5.js library. The study employed project analysis and semi-structured student interviews to assess learning outcomes. Findings suggest that AI-enhanced learning can effectively support programming education without increasing instructional hours, providing insights for integrating AI concepts into existing curricula. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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