Submit to J. Intell. Review for J. Intell. Propose a Special Issue

Journal Menu

Journal Browser

Advances of AI in Talent Development: Synergies Between Creativity, Cognitive Intelligence, and Socio-Emotional Growth

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Journal of Intelligence (ISSN 2079-3200). This special issue belongs to the section "Studies on Cognitive Processes".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 10515

Share This Special Issue

Special Issue Editors

Prof. Dr. Yadan Li

E-Mail Website
Guest Editor

Ministry of Education Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi'an, China
Interests: cognitive and neural mechanisms of creative ideation; the impact of artificial intelligence on innovation talent cultivation and associated neurocognitive mechanisms; development, enhancement, and neural plasticity of creativity via computational approaches

Prof. Dr. Jacques Grégoire

E-Mail Website
Guest Editor

Institute of Psychology, University of Louvain, 10 Place du Cardinal Mercier, 1348 Louvain-la-Neuve, Belgium
Interests: intelligence; giftedness; intellectual assessment; mathematical learning; mathematical disabilities; mathematical and scientific creativity; intelligence and emotion

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, “Advances of AI in Talent Development: Synergies Between Creativity, Cognitive Intelligence, and Socio-Emotional Growth.” This issue aims to explore the evolving intersection between artificial intelligence and talent development, with a particular focus on how AI technologies can enhance human potential across multiple domains—cognitive, creative, and emotional.

The Special Issue welcomes contributions that investigate how AI tools—such as machine learning, adaptive learning systems, and generative AI—support the identification, nurturing, and assessment of talent. Topics may include AI-assisted creativity development, emotional intelligence modeling, personalized learning algorithms, and the role of AI in fostering social–emotional learning.

By situating this Special Issue within the existing literature on AI in education, creativity studies, and cognitive development, we aim to bridge disciplinary gaps and highlight innovative applications of AI in talent cultivation. This collection will provide a platform for theoretical, empirical, and practice-based contributions that explore ethical considerations, effectiveness, and future implications.

We welcome original research articles, reviews, conceptual papers, and case studies from interdisciplinary perspectives, including education, psychology, AI, neuroscience, and human development.

We look forward to receiving your contributions.

Prof. Dr. Yadan Li
Prof. Dr. Jacques Grégoire
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Intelligence is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
talent development
creativity and innovation
cognitive intelligence
social-emotional learning
personalized learning
human–AI collaboration
adaptive education technologies
emotional intelligence modeling

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

15 pages, 1524 KB

Open AccessArticle

Developing Talent with Artificial Intelligence: Human–AI Symbiotic Theory (HAIST) as a Framework for AI-Mediated Learning and Talent Development

by John C. Chick and Laura Thomsen Morello

J. Intell. 2026, 14(5), 86; https://doi.org/10.3390/jintelligence14050086 - 19 May 2026

Viewed by 200

Abstract

Traditional talent development models were designed before the AI revolution and do not consider artificial agents as possible sources of development. artificial intelligence is quickly infiltrating education spaces—but our thinking about learning has not caught up with how we can productively pair learners with both human and artificial intelligence. Addressing this gap, we introduce Human–AI Symbiotic Theory (HAIST), a novel theoretical framework designed for AI-facilitated environments, which posits how learners can productively leverage both humans and AI as “development partners” across the entire talent development process. We begin with a comprehensive integration of ideas and theory from the literature on talent development, AI for learning, and human–AI collaboration and use these insights to build HAIST for the specific context of talent development. HAIST comprises three mechanisms—Complementary Intelligence Activation (CIA), Dynamic Adaptive Co-Regulation (DACR), and Agency-Preserving Scaffolding (APS)—that are grounded in prior theory and research on topics like sociocultural theory, self-regulated learning, and distributed cognition. We then demonstrate how HAIST can be applied throughout all phases of talent development while highlighting implications for traditionally underserved learners like adult learners, student veterans, multilingual learners, and first-generation learners. We provide an applied example of how the three mechanisms work in tandem to support talent development and discuss points of tension that must be navigated when applying HAIST (e.g., between adaptation and optimization vs. agency). Lastly, we highlight how considerations of ethics and learner rights (algorithmic bias, learner voice, etc.) should be considered when operationalizing HAIST. Overall, HAIST can serve as a foundational theory to not only understand how talent development should occur between learners and both humans and AI, but also to consider the process of instruction design in AI-mediated learning environments. Full article

(This article belongs to the Special Issue Advances of AI in Talent Development: Synergies Between Creativity, Cognitive Intelligence, and Socio-Emotional Growth)

► Show Figures

Figure 1

18 pages, 1368 KB

Open AccessArticle

The Influence of AI on Critical Thinking and Creativity in L2 Learning Contexts: A Social Cognitive Perspective

by Yilong Yang, Shuyi Zhang and Yadan Li

J. Intell. 2026, 14(5), 78; https://doi.org/10.3390/jintelligence14050078 - 2 May 2026

Viewed by 389

Abstract

The expanding role of artificial intelligence (AI) in education raises important questions about how AI-supported learning may foster higher-order thinking and creative talent development. Guided by social cognitive theory, the current research examined how AI self-efficacy predicts creativity among second language (L2) learners through the mediating roles of AI literacy and critical thinking disposition. Two substudies were conducted. Study 1 (N = 72) tested a simple mediation model and demonstrated that AI self-efficacy positively predicted creativity both directly and indirectly through AI literacy. Study 2 (N = 135) extended these findings by incorporating critical thinking disposition and by using another measure of creativity. Results showed that AI self-efficacy positively predicted creativity, and this relationship was mediated independently by AI literacy and critical thinking disposition, as well as sequentially through both factors. The current study provides empirical evidence for pathways linking AI self-efficacy, AI literacy, critical thinking disposition, and creativity in AI-supported L2 learning. It highlights the importance of reflective and critical use of AI tools in language education. Full article

(This article belongs to the Special Issue Advances of AI in Talent Development: Synergies Between Creativity, Cognitive Intelligence, and Socio-Emotional Growth)

► Show Figures

Figure 1

23 pages, 1430 KB

Open AccessArticle

Are We Helping Workers Reskill for the Future of Work? Using AI to Explore the Alignment of Online Course Offerings and Job Skill Requirements

by Makai A. Ruffin, Margaret E. Beier, Felix Y. Wu, Nathaniel M. Voss, Anoop A. Javalagi and Harrison J. Kell

J. Intell. 2026, 14(4), 59; https://doi.org/10.3390/jintelligence14040059 - 1 Apr 2026

Viewed by 1292

Abstract

Millions of workers and job seekers turn to online platforms to gain work-relevant skills to remain competitive for the future of work. However, little is known about whether the skills acquired in work-relevant online courses align with the skills required for 21st-century jobs. Drawing on literature on job and skill matching, this exploratory study examines the alignment between available online training and learning content and the skills demanded by jobs (i.e., training-skills demands fit) using artificial intelligence methods. A large language model (LLM; Claude Haiku 3.5) was instructed to evaluate which of the 35 basic and cross-functional skills from the Occupational Information Network (O*NET) could be acquired in a given course, which was based on 2549 course descriptions extracted from MIT OpenCourseWare. Linkages between online training and skills were broken down by job family and occupations with a bright outlook designation (i.e., occupations estimated to have 75,000 or more job openings between 2024 and 2034 across the United States). Results suggest that the skill of active learning (i.e., using new information for problem-solving; 88%, N = 2242) was linked to the highest number of online courses, whereas the skill of instructing (i.e., teaching others to perform tasks; 5.3%, N = 134) was linked to the least. Computer and mathematical occupations had the highest proportion of courses wherein individuals can acquire basic and cross-functional skills, whereas food preparation and serving occupations had the lowest proportion of courses. Non-bright outlook occupations had a significantly lower proportion of online courses where individuals can acquire basic and cross-functional skills compared to occupations with a bright outlook designation. We expand on existing skills-matching perspectives to consider how training-skills demands fit can constrain or facilitate continuous learning and development. Further, we illustrate how LLMs can be used to efficiently and at scale summarize descriptive information on talent development issues. Full article

(This article belongs to the Special Issue Advances of AI in Talent Development: Synergies Between Creativity, Cognitive Intelligence, and Socio-Emotional Growth)

► Show Figures

Figure 1

30 pages, 1702 KB

Open AccessArticle

The Role of Generative Artificial Intelligence in Developing Cognitive and Research Talent Among Postgraduate Students

by Asem Mohammed Ibrahim, Reem Ebraheem Saleh Alhomayani and Azhar Saleh Abdulhadi Al-Shamrani

J. Intell. 2026, 14(4), 53; https://doi.org/10.3390/jintelligence14040053 - 26 Mar 2026

Viewed by 878

Abstract

Generative Artificial Intelligence (GAI) is rapidly transforming higher education by introducing new mechanisms for supporting the development of advanced cognitive processes and research-related capabilities. This study examines how postgraduate students employ GAI to develop their cognitive and research talent, conceptualized here as higher-order academic skills such as analysis, synthesis, and critical reasoning, across six domains: literature review, theoretical development, research design, data analysis, academic writing, ethical use, and challenges encountered—signaled explicitly rather than listed line by line. We administered a validated multidimensional scale to 214 postgraduate students, and the results indicate a moderate overall use of GAI, with notably high involvement in practices that emphasize ethics and responsibility. Students reported clear cognitive benefits in tasks involving information processing, linguistic refinement, and conceptual clarification while showing caution toward delegating higher-order analytical or theoretical reasoning to AI systems. Key challenges included limited institutional training, concerns about data privacy and academic integrity, and difficulties evaluating the originality and reliability of AI-generated content. Inferential analyses indicated significant differences based on gender, academic level, and general technology proficiency, whereas no differences emerged across age groups, departments, or specializations. Overall, this study demonstrates how GAI can contribute to the development of higher-level cognitive skills and research competencies, with “moderate use” operationalized as consistent but selective engagement across domains, while underscoring the need for structured training, clear guidelines, and teaching approaches that foster the responsible and effective incorporation of AI within postgraduate research. The results highlight practical implications for higher education, including the importance of institutional training programs, governance frameworks for responsible AI use, and pedagogical models that foster critical engagement with GAI. Full article

(This article belongs to the Special Issue Advances of AI in Talent Development: Synergies Between Creativity, Cognitive Intelligence, and Socio-Emotional Growth)

► Show Figures

Figure 1

22 pages, 1172 KB

Open AccessArticle

The ATHENA Competency Framework: An Evaluation of Its Validity According to Instructional Designers and Human Resource Development Professionals

by Jeremy Lamri, Karin Valentini, Felipe Zamana and Todd Lubart

J. Intell. 2026, 14(2), 23; https://doi.org/10.3390/jintelligence14020023 - 3 Feb 2026

Viewed by 629

Abstract

The ATHENA (Advanced Tool for Holistic Evaluation and Nurturing of Abilities) competency framework proposes a multidimensional approach to human performance structured around five interdependent dimensions (cognition, conation, knowledge, emotion, and sensori-motion), operationalized through 60 fine-grained facets. Although ATHENA is grounded in contemporary psychological theory and supported conceptually by multivariate research in intelligence, creativity, and skill acquisition, empirical evidence regarding the clarity and practical comprehensibility of its facets remains limited. This study investigates the extent to which instructional designers and human resource development (HRD) professionals—two groups who routinely operationalize competencies for learning, assessment, and workforce development—understand and evaluate the semantic clarity and usability of the 60 facets. Seventy-five practitioners completed a structured evaluation of the ATHENA framework facets, which are designed to be used in a hybrid intelligence system for competency management. This article presents the theoretical background, methodological design, and results concerning users’ comprehension of the framework’s components. The findings support, in general, the compatibility of ATHENA’s facets and practitioners’ conceptions. Full article

(This article belongs to the Special Issue Advances of AI in Talent Development: Synergies Between Creativity, Cognitive Intelligence, and Socio-Emotional Growth)

► Show Figures

Figure 1

Review

Jump to: Research

21 pages, 2817 KB

Open AccessReview

Does Generative Artificial Intelligence Improve Students’ Higher-Order Thinking? A Meta-Analysis Based on 29 Experiments and Quasi-Experiments

by Yan Zhao, Yuhe Yue, Zhonghua Sun, Qiang Jiang and Gangsheng Li

J. Intell. 2025, 13(12), 160; https://doi.org/10.3390/jintelligence13120160 - 5 Dec 2025

Cited by 6 | Viewed by 5921

Abstract

The widespread application of Generative Artificial Intelligence (Gen-AI) is transforming educational practices and driving pedagogical innovation. While cultivating higher-order thinking (HOT) represents a central educational goal, its achievement remains an ongoing challenge. Current evidence regarding the impact of Gen-AI on HOT is relatively fragmented, lacking systematic integration, particularly in the analysis of moderating variables. To address this gap, a meta-analysis approach was employed, integrating data from 29 experimental and quasi-experimental studies to quantitatively assess the overall impact of Gen-AI on learners’ HOT and to examine potential moderating factors. The analysis revealed that Gen-AI exerts a moderate positive effect on HOT, with the most significant improvement observed in problem-solving abilities, followed by critical thinking, while its effect on creativity is relatively limited. Moderation analyses further indicated that the impact of Gen-AI is significantly influenced by experimental duration and learners’ self-regulated learning (SRL) abilities: effects were strongest when interventions lasted 8–16 weeks, and learners with higher SRL capacities benefited more substantially. Based on the research findings, this study proposed that Gen-AI should be systematically integrated as a targeted instructional tool to foster HOT. Medium- to long-term interventions (8–16 weeks) are recommended to enhance learners’ problem-solving and critical thinking abilities. At the same time, effective approaches should also be explored to promote creative thinking through Gen-AI within existing pedagogical frameworks. Furthermore, individual learner differences should be accounted for by adopting dynamic and personalized scaffolding strategies to foster SRL, thereby maximizing the educational potential of Gen-AI in cultivating innovative talents. Full article

(This article belongs to the Special Issue Advances of AI in Talent Development: Synergies Between Creativity, Cognitive Intelligence, and Socio-Emotional Growth)

► Show Figures

Journal Menu

Journal Browser

Advances of AI in Talent Development: Synergies Between Creativity, Cognitive Intelligence, and Socio-Emotional Growth

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (6 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI