AI Adoption in Social Science Education: Personality, Student Engagement, and User Experience on Digital Platforms

Topic Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) into social science education redefined pedagogical thought, developed possibilities for increased digital interaction and personalized learning. The evolution of tools and technologies makes the connection between humans and AI crucial. Students by using these tools (from chat gpt to algorithm-based social networking platforms) encounter a new digital learning ecosystem. AI should not function as a substitute for humans and pedagogical dominance, but instead should be used for human-centered reinforcement.

This multidisciplinary topic examines AI usage from two perspectives: individual student behaviors and institutional responses. At the individual level, the identification of personality traits, preferences in User Experience (UX) and User Interface (UI) design and cognitive preferences of students, influence their engagement with AI platforms. At the institutional level, it explores how universities develop training programs, guidelines, and governance frameworks that balance academic integrity with innovation. Educational institutions should meet student needs with ethical guidance for AI literacy. Understanding how AI adapts to needs, ensures that digital transformation meets the needs of society in an effective, inclusive and ethically responsible way. Connecting psychology with educational technology, organizational management, and exploring how user experience design and AI-assisted feedback enhance motivation and engagement in learning on social media and formal educational platforms. Equally important is the ethical use of AI in academic work and exploring the balance of academic integrity and AI assistance based on individual differences.

We encourage research on institutional policy effectiveness, faculty adoption barriers, student AI anxiety and literacy, cross-platform learning behaviors, comparative and cross-cultural studies on the adoption and impact of AI tools in different countries and regions, based on cultural, institutional, and socioeconomic contexts. We urge original research, case studies, and theory papers on the place of personality in AI acceptance, strategies for social presence in a machine-aided environment, and the moral, legal, and social considerations of “human-in-the-loop” methods. We welcome interdisciplinary perspectives from psychology, education, sociology, management, and communication studies to create a more comprehensive picture of the future in social science education.

We look forward to receiving your contributions.

Best Regards,

Dr. Stavros Kaperonis
Dr. Eirini Karakasidou
Dr. Fani Gkritseli
Dr. Panagiotis A. Tsaknis
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Education Sciences education	3.5	6.2	2011	24.8 Days	CHF 2000	Submit
Social Sciences socsci	2.0	3.5	2012	30.3 Days	CHF 1800	Submit
Societies societies	2.2	3.8	2011	29.7 Days	CHF 1600	Submit

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Published Papers (8 papers)

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23 pages, 899 KB  

Open AccessArticle

Perceived Anthropomorphism as a Mediating Mechanism in Human–AI Interaction: Explaining Empathy, Trust, and Perceived Value in Higher Education

by Mohammed Al-Mamari and Abdullah Al-Abri

Educ. Sci. 2026, 16(9), 1558; https://doi.org/10.3390/educsci16091558 (registering DOI) - 20 Sep 2026

Abstract

Artificial Intelligence (AI) is increasingly evaluated as both a functional technology and a socially responsive interaction partner. This study examines how academic staff form trust in educational AI and how that trust contributes to perceived value. Its central contribution is to position perceived [...] Read more.

Artificial Intelligence (AI) is increasingly evaluated as both a functional technology and a socially responsive interaction partner. This study examines how academic staff form trust in educational AI and how that trust contributes to perceived value. Its central contribution is to position perceived anthropomorphism as a mediating mechanism through which perceived animacy and perceived intelligence are translated into perceived empathy. A cross-sectional survey was completed by 236 academic staff members, and the model was tested using partial least squares structural equation modeling. The measurement model demonstrated satisfactory reliability and validity. A competing-path analysis provided a more differentiated account of the proposed mediation mechanism. In the final competing-path model, Perceived Usefulness remained the strongest predictor of Perceived Value (β = 0.586), while Perceived Intelligence strongly predicted Perceived Usefulness (β = 0.570) and also had a substantial direct effect on Perceived Empathy (β = 0.465). Perceived Anthropomorphism remained positively associated with Perceived Empathy (β = 0.238). The direct effect of Perceived Animacy on Perceived Empathy was not significant (β = 0.104, p = 0.154), whereas its indirect effect through Perceived Anthropomorphism remained significant (β = 0.095, p = 0.006), indicating indirect-only mediation. For Perceived Intelligence, both the indirect effect through Perceived Anthropomorphism (β = 0.057, p = 0.020) and the direct effect on Perceived Empathy were significant, indicating complementary partial mediation. Perceived Usefulness and Trust in AI jointly explained 57.3% of the variance in Perceived Value (R² = 0.573). The findings reveal two complementary routes to valuing educational AI: a functional–cognitive route centered on intelligence and usefulness, and a social–emotional route through which anthropomorphism converts AI features into perceived empathy. Full article

(This article belongs to the Topic AI Adoption in Social Science Education: Personality, Student Engagement, and User Experience on Digital Platforms)

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18 pages, 3746 KB  

Open AccessArticle

Cohort Differences in University Students’ AI Readiness, Anxiety, and Adoption Intention: A Comparison of the 2022 and 2024 Survey Cohorts

by Mohamad Izani, Amr Assad, Nada Abdul Baki, Akhmed Kaleel, Mohammed Fyadh, Areen Al-Zoubi and Mona Gabr

Educ. Sci. 2026, 16(9), 1539; https://doi.org/10.3390/educsci16091539 (registering DOI) - 18 Sep 2026

Viewed by 148

Abstract

The diffusion of generative artificial intelligence (AI) has raised questions about university students’ readiness to use it. We conducted a secondary analysis of 1205 records from 1146 students across eight countries and three survey waves (2022–2024). Fifty-nine students participated in both the 2022 [...] Read more.

The diffusion of generative artificial intelligence (AI) has raised questions about university students’ readiness to use it. We conducted a secondary analysis of 1205 records from 1146 students across eight countries and three survey waves (2022–2024). Fifty-nine students participated in both the 2022 and 2024 waves; analyses therefore used student-clustered inference. The primary comparison involved Slovak information-technology undergraduates surveyed in 2022 (186 records, all collected before the 30 November 2022 public release of ChatGPT-5) and 2024 (180 records). The 2024 cohort reported higher behavioural intention (d = 0.31; Holm-adjusted p = 0.024) and lower self-rated AI literacy (d = −0.36; Holm-adjusted p = 0.004); anxiety was higher only in supporting multi-country and latent analyses. Equality constraints produced little additional deterioration in the invariance models, but the imperfect configural fit, including full-model CFI = 0.812 and TLI = 0.796 and weak behavioural intention fit, limits the strength of this evidence. AI literacy additionally had α = 0.69, AVE = 0.48, and only partial scalar invariance, so its cohort difference is exploratory. Discriminant validity was mixed: HTMT exceeded 0.85 for relevance–career motivation (0.872) and intrinsic motivation–satisfaction (0.856), but no pair exceeded 0.90 and prespecified collapsed-factor models fitted worse than the ten-factor model. The principal standardised multiple regression explained 65.2% of behavioural-intention variance; demographic, educational, national, and temporal adjustment (R² = 0.681) and parsimonious nonlinear sensitivity analyses preserved the central coefficient pattern. These repeated cross-sectional results describe cohort differences and associations, not causal effects of ChatGPT or any other tool. Full article

(This article belongs to the Topic AI Adoption in Social Science Education: Personality, Student Engagement, and User Experience on Digital Platforms)

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20 pages, 629 KB  

Open AccessArticle

Smartphone Use and Environmental Sustainability Awareness and Practices Among Students of Al Ain University: A Cross-Sectional Correlational Study

by Shirin Saleh Alodwan, Hatem AlQudah, Hussein Al-Srehan and Dua’a Aladwan

Societies 2026, 16(9), 288; https://doi.org/10.3390/soc16090288 - 8 Sep 2026

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Abstract

The purpose of this study is to examine the impact of the use of smartphones on the students’ environmental awareness and the adoption of environmental sustainability practices at the University of Al Ain. A descriptive analytical approach was used and a questionnaire was [...] Read more.

The purpose of this study is to examine the impact of the use of smartphones on the students’ environmental awareness and the adoption of environmental sustainability practices at the University of Al Ain. A descriptive analytical approach was used and a questionnaire was employed to gather data from a voluntary non-probability sample of 90 students from different academic programs at the university. The research focuses on five main areas: students’ environmental sustainability awareness, perceived positive uses of smartphones, perceived negative environmental consequences of smartphone use, the association between smartphone use and sustainable practices, and suggestions for improving smartphone-supported environmental sustainability. The results showed that students were at a medium level of environmental awareness, both in theory and practice, as they had a good understanding in theory but were not actively involved in environmental activities. In terms of positive effects, students demonstrated their ability to use smartphones as tools to raise awareness and promote sustainable practices, including using less paper and taking part in digital environmental efforts. But the problems were that there was more e-waste because phones were being replaced so often and energy consumption was unnecessary. The study revealed a moderate positive correlation between the use of smartphones and the adoption of sustainable practices, indicating the role of smartphones in environmental activities. The study recommends some solutions, including creating an environmental awareness app, holding digital competitions, etc. It suggests raising awareness among students regarding recycling and electronic waste and promoting the use of smartphones in a sustainable manner for a positive impact on the environment. Full article

(This article belongs to the Topic AI Adoption in Social Science Education: Personality, Student Engagement, and User Experience on Digital Platforms)

30 pages, 4520 KB  

Open AccessArticle

Innovation, Ethics and the Responsible Use of AI in Higher Education: Evidence from an Exploratory Study of 30 Universities Worldwide

by Ida Cortoni

Soc. Sci. 2026, 15(9), 602; https://doi.org/10.3390/socsci15090602 - 7 Sep 2026

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Abstract

The integration of AI into everyday life, across work and leisure, constitutes a significant driver of social innovation, reshaping interaction, communication, learning processes, and knowledge organisation, as well as the management of work and organisational activities. Increasingly oriented towards users’ needs, AI systems [...] Read more.

The integration of AI into everyday life, across work and leisure, constitutes a significant driver of social innovation, reshaping interaction, communication, learning processes, and knowledge organisation, as well as the management of work and organisational activities. Increasingly oriented towards users’ needs, AI systems are learned and instructed to support not only cognitive but also emotional demands. In higher education, AI represents a transformative force, enhancing institutional efficiency across research, teaching, and third-mission activities. However, its adoption raises critical ethical concerns and highlights the importance of digital literacy in fostering soft competences. These are essential to promote awareness, self-regulation, and a critical, responsible use of AI technologies. Key questions remain regarding the extent of AI integration within universities, its ethical regulation, and its role in supporting academic and administrative practices. This article offers an initial overview through the analysis of 30 global case studies conducted within a European Commission-funded project. Building on Escudero’s theoretical framework of Imperfect AI, the analysis of the research findings highlights the discrepancies between the macro-social perspective on the ethical integration of AI within institutions and the meso- and micro-social perspectives on its effective integration into the everyday practices of institutional actors. Full article

(This article belongs to the Topic AI Adoption in Social Science Education: Personality, Student Engagement, and User Experience on Digital Platforms)

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24 pages, 308 KB  

Open AccessArticle

The Impact of Social Media on Mental Clutter Among Students at Al Ain University—A Field Study

by Shirin AlOdwan, Hatem Alqudah, Hussein Al-Srehan and Dua’a Aladwan

Educ. Sci. 2026, 16(9), 1461; https://doi.org/10.3390/educsci16091461 - 7 Sep 2026

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Abstract

This study aimed to look into the correlation between the use of Instagram and TikTok and mental clutter among students of Al Ain University. The descriptive-correlational design was used, and the data were gathered by questionnaire from 70 students. The results indicated that [...] Read more.

This study aimed to look into the correlation between the use of Instagram and TikTok and mental clutter among students of Al Ain University. The descriptive-correlational design was used, and the data were gathered by questionnaire from 70 students. The results indicated that students used Instagram and TikTok moderately and that they perceived moderate influence on their concentration, academic focus and mental clarity. The research calls for university policies on digital wellness, awareness workshops, limiting the use of phones during studies and student support programs to mitigate the negative impact of overuse of social media on academic and psychological performance. The data were collected using a questionnaire consisting of two sections: the first deals with personal variables, and the second contains 15 items distributed over three main axes. The results showed that the degree of students’ use of social media programs was average, as students used these applications on a daily basis but moderately. The effect of excessive use of social media software on distraction was moderate, as students found it difficult to focus on their assignments due to excessive use of these applications. Regarding the suggestions to minimize negative impacts, students pointed out the importance of setting certain times to stay away from these applications while studying. The study concluded that students should be aware of the negative effects of excessive use of social media programs and provide strategies to reduce these effects. Quantitatively, the overall Instagram and TikTok use score was M = 2.21 (SD = 0.897), the perceived mental-clutter score was M = 2.28 (SD = 0.891), and the strategy score was M = 2.29 (SD = 0.831); the full questionnaire showed good internal consistency (Cronbach’s alpha = 0.85). One-sample tests against the three-point scale midpoint of 2.00 showed that perceived mental clutter, t(69) = 2.63, p = 0.011, and support for self-regulation strategies, t(69) = 2.92, p = 0.005, were significantly above the midpoint, whereas the social-media-use mean was marginal, t(69) = 1.96, p = 0.054. Full article

(This article belongs to the Topic AI Adoption in Social Science Education: Personality, Student Engagement, and User Experience on Digital Platforms)

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22 pages, 1183 KB  

Open AccessArticle

Recrafting Financial Learning with ChatGPT: A Co-Constructivist Model of Knowledge

by Christophe Schinckus, Felicia H. L. Chong and Amir Hajbaba

Educ. Sci. 2026, 16(8), 1253; https://doi.org/10.3390/educsci16081253 - 7 Aug 2026

Viewed by 502

Abstract

The rapid expansion of ChatGPT in higher education and the increasing adoption of generative AI in the financial industry raise important pedagogical questions for finance education. While existing studies often examine the outcomes, risks, or technical affordances of AI-supported learning, less attention has [...] Read more.

The rapid expansion of ChatGPT in higher education and the increasing adoption of generative AI in the financial industry raise important pedagogical questions for finance education. While existing studies often examine the outcomes, risks, or technical affordances of AI-supported learning, less attention has been given to how ChatGPT may reshape the process through which students construct, apply, and evaluate financial knowledge. This conceptual paper develops a co-constructivist model of ChatGPT-induced financial education. The paper adopts a conceptual research design and deductive construct-mapping approach, using the epistemic categories of episteme (theoretical knowledge), techne (practical application), and epitaktike (directing or commanding knowledge). Through this framework, ChatGPT is conceptualized as a Socratic and dialogical platform that can mediate between financial theory, applied reasoning, and responsible judgement. The paper also proposes a co-constructivist grading rubric that pedagogically operationalizes these epistemic dimensions into observable learning behaviours. This article aims at laying down the conceptual foundations of a co-constructivist ChatGPT-supported student-centred, and ethically guided financial learning environment. Full article

(This article belongs to the Topic AI Adoption in Social Science Education: Personality, Student Engagement, and User Experience on Digital Platforms)

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18 pages, 1873 KB  

Open AccessArticle

Measuring AI Usage in Geometry Learning Among Pre-Service Mathematics Teachers: Scale Development and Institutional Comparison in Indonesia

by Muhammad Ammar Naufal, Nurfaida Tasni, Andi Syukriani, Anny Sovia and Hamzah Upu

Educ. Sci. 2026, 16(8), 1248; https://doi.org/10.3390/educsci16081248 - 6 Aug 2026

Viewed by 555

Abstract

Pre-service mathematics teachers engage with AI as both geometry learners and future teachers, yet domain-specific instruments for measuring this usage remain scarce. This study developed and validated the AI Usage in Geometry Learning Scale (AIGLS) and compared usage between two Indonesian teacher-education institutions. [...] Read more.

Pre-service mathematics teachers engage with AI as both geometry learners and future teachers, yet domain-specific instruments for measuring this usage remain scarce. This study developed and validated the AI Usage in Geometry Learning Scale (AIGLS) and compared usage between two Indonesian teacher-education institutions. A comparative survey covered 178 pre-service mathematics teachers at Universitas Negeri Makassar (UNM, n = 89) and Universitas Negeri Jakarta (UNJ, n = 89). The AIGLS operationalises five dimensions: Conceptual Understanding Support, Problem-Solving Assistance, Geometric Visualisation, Pedagogical Readiness, and Critical Evaluation of AI. A ten-expert panel established content validity (CVI = 0.976). Item analysis excluded all five negatively worded items; all analyses therefore used the final 20-item scale, which showed good full-scale (Cronbach’s α = 0.873) and subscale (α = 0.591–0.810) reliability. ChatGPT was the dominant tool (96.6%), and UNM reported significantly higher usage intensity than UNJ (χ²(4) = 27.917, p < 0.001). Mann–Whitney U tests with Bonferroni correction confirmed UNM scored higher overall (U = 4749.5, p = 0.022, r_rb = −0.20), with Geometric Visualisation showing the largest institutional gap (r_rb = −0.38, medium effect). The validated AIGLS supports measuring AI usage in geometry teacher preparation and informs AI-literacy policy. Full article

(This article belongs to the Topic AI Adoption in Social Science Education: Personality, Student Engagement, and User Experience on Digital Platforms)

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12 pages, 204 KB  

Open AccessArticle

Artificial Intelligence Professional Development Provision for Teachers in Andalusia: Documentary Analysis and Recommendations for a Comprehensive Approach

by Manuel Reina-Parrado, Pedro Román-Graván and Carlos Hervás-Gómez

Soc. Sci. 2026, 15(7), 449; https://doi.org/10.3390/socsci15070449 - 6 Jul 2026

Viewed by 430

Abstract

This study analyses the professional development provision in artificial intelligence (AI) offered to non-university teachers in Andalusia during the 2023–2024 academic year. The research follows a descriptive documentary design based on the systematic review of training activities provided by the Teacher Training Centres [...] Read more.

This study analyses the professional development provision in artificial intelligence (AI) offered to non-university teachers in Andalusia during the 2023–2024 academic year. The research follows a descriptive documentary design based on the systematic review of training activities provided by the Teacher Training Centres (CEPs) in the eight Andalusian provinces. The initial corpus comprised 1832 training activities. After screening course titles and, when available, objectives, descriptions, metadata and content lists, 26 AI-related activities were identified; after excluding repeated editions and activities whose information was not accessible, the final analysable corpus consisted of 15 distinct and visible courses. The analysis combined quantitative description of distribution by province with qualitative content analysis supported by Atlas.ti. The results show that AI-related training represented only 1.42% of the overall offer and that the final analysable corpus represented 0.82% of all training activities. The provision was also unevenly distributed across provinces and tended to privilege specific tools, generative AI applications and productivity-oriented uses, with comparatively limited attention to ethical, pedagogical, assessment-related and interdisciplinary dimensions. The findings suggest the need for a more coherent regional strategy based on common minimum AI literacy modules, differentiated pathways by educational stage, blended and mentored formats, classroom-based projects and systematic evaluation of training impact. Full article

(This article belongs to the Topic AI Adoption in Social Science Education: Personality, Student Engagement, and User Experience on Digital Platforms)

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