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32 pages, 576 KB

Open AccessArticle

Learning Pathways and Credential Signals in Online Graduate Micro-Credentialing: An OpenAlex Evidence Map

by Justin C. Pettijohn

Knowledge 2026, 6(2), 11; https://doi.org/10.3390/knowledge6020011 - 27 May 2026

Viewed by 181

Abstract

Online graduate micro-credentials are promoted both as flexible learning pathways for working professionals and as portable signals of capability for employers and professional communities. Yet, scholarship on these credentials is dispersed across policy, education, technology, and workforce literatures, making it difficult to see [...] Read more.

Online graduate micro-credentials are promoted both as flexible learning pathways for working professionals and as portable signals of capability for employers and professional communities. Yet, scholarship on these credentials is dispersed across policy, education, technology, and workforce literatures, making it difficult to see how the field is framed and where evidence is accumulating. This study uses OpenAlex to build an updateable evidence map of online graduate micro-credentialing. A total of 2535 records (2010–2026) were retrieved and deduplicated to 2150 works. The corpus was annotated with a transparent seedless triage step. A conservatively revised keyword typology was then applied to a typology-eligible subset, and topic modeling was used to surface candidate themes. Within the typology-eligible subset, 223 records were classifiable. Learning-first framings (66.8%) and stackable framings (58.7%) remained more common, and a 100-record hand-coded audit supported the revised rules (80.0% full-quadrant agreement). Large thematic clusters concern workforce/economic skills, engagement-oriented digital learning, and broad online teaching/learning, while smaller badge-related, infrastructure, and adjacent-domain clusters require cautious interpretation. The map points to a literature still weighted toward pathway design and implementation, but typology validation also indicates that structural framing is more mixed than the earlier always-assigned counts suggested. By making the search space and annotation logic transparent, this study provides a rerunnable baseline for cumulative qualitative synthesis and a clearer agenda for future research on how online graduate micro-credentials function as both learning experiences and credential signals. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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24 pages, 307 KB

Open AccessArticle

Using Sport-Specific State-Based Mental Models to Scaffold Introductory Java Learning for Student-Athletes: A Wrestling-Inspired Conceptual and Pedagogical Framework

by Qing Zhang and Jizhou Tong

Knowledge 2026, 6(2), 10; https://doi.org/10.3390/knowledge6020010 - 12 May 2026

Viewed by 247

Abstract

Introductory Java programming requires learners to reason about abstract computational concepts such as program state, control flow, and execution order, which often present substantial difficulties for novice programmers. These challenges may be further intensified for collegiate student athletes when programming instruction remains disconnected [...] Read more.

Introductory Java programming requires learners to reason about abstract computational concepts such as program state, control flow, and execution order, which often present substantial difficulties for novice programmers. These challenges may be further intensified for collegiate student athletes when programming instruction remains disconnected from the domain knowledge that shapes their prior experiences. This paper proposes a wrestling-inspired, state-based pedagogical framework that leverages the rule system of National Collegiate Athletic Association (NCAA) wrestling as an analogical knowledge domain for introducing foundational Java programming concepts. Within this framework, wrestling match states and scoring actions are systematically mapped to core programming constructs, which include variable assignment, conditional branching, loops, method invocation, and program termination. This paper is positioned as a conceptual and pedagogical framework study rather than an empirical intervention study. It focuses on the theoretical rationale, conceptual alignment, instructional mappings, and classroom implementation possibilities of a wrestling-inspired approach. This paper does not report participant data, learning assessments, or comparative outcome measures. Instead, it illustrates how sport-specific mental models can be transformed into structured instructional representations that may support learners’ reasoning about program execution. By integrating domain-aligned cognitive schemas with programming instruction, the proposed framework offers a structured knowledge scaffolding approach that is designed to support novice understanding of computational processes in introductory programming education. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

17 pages, 1223 KB

Open AccessArticle

Factors Driving Study Efficiency Gains and Exam Readiness from ChatGPT Use Among STEM Students: A Machine Learning Analysis

by Vishnu Kumar

Knowledge 2026, 6(1), 7; https://doi.org/10.3390/knowledge6010007 - 4 Mar 2026

Viewed by 698

Abstract

This study examines the factors driving perceived Study Efficiency and Exam Readiness associated with ChatGPT use among STEM students in higher education. Although prior research on generative artificial intelligence (GenAI) has largely focused on adoption and attitudes using descriptive or linear statistical approaches, [...] Read more.

This study examines the factors driving perceived Study Efficiency and Exam Readiness associated with ChatGPT use among STEM students in higher education. Although prior research on generative artificial intelligence (GenAI) has largely focused on adoption and attitudes using descriptive or linear statistical approaches, limited empirical work has explored how students’ interactions with such tools relate to learning-related outcomes. To address this gap, this study applies an interpretable machine learning (ML) framework to identify key predictors of learning gains from ChatGPT use. Data were obtained from a large-scale global survey of STEM students (n = 10,525) across 109 countries and territories, capturing usage patterns, perceived capabilities, satisfaction, and academic outcomes. Two eXtreme Gradient Boosting (XGBoost)-based ML classification models were developed to predict Study Efficiency and Exam Readiness, and SHapley Additive exPlanations (SHAP) were used to interpret feature-level contributions. The models achieved strong predictive performance for the high-gain class, with an accuracy of 0.93 (F1 = 0.96) for Study Efficiency and 0.86 (F1 = 0.92) for Exam Readiness. Results indicate that motivation, personalized learning support, improved access to knowledge, facilitation of study activities, and exam-focused study assistance are key predictors of learning gains. These findings offer empirical and practical insights for educators and policymakers seeking to design effective and pedagogically sound AI-assisted learning environments in STEM education. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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12 pages, 962 KB

Open AccessArticle

Students’ Utilisation of Artificial Intelligence in Open and Distance Learning

by Belingtone Eliringia Mariki

Knowledge 2026, 6(1), 6; https://doi.org/10.3390/knowledge6010006 - 25 Feb 2026

Viewed by 1004

Abstract

The use of Artificial Intelligence (AI) in learning is expanding globally; however, the full potential of AI tools in the Open and Distance Learning (ODL) context, particularly at the Institute of Adult Education (IAE), remains underexplored. This study examined the IAE ODL students’ [...] Read more.

The use of Artificial Intelligence (AI) in learning is expanding globally; however, the full potential of AI tools in the Open and Distance Learning (ODL) context, particularly at the Institute of Adult Education (IAE), remains underexplored. This study examined the IAE ODL students’ perspectives on the use of AI tools in learning. Specifically, it investigated ODL students’ familiarity with AI, AI preferences and use in learning, and perspectives on AI tool use in ODL. The study employed a mixed-methods approach, utilising a convergent parallel design to collect data from 93 second- and third-year ODL students at the Dar es Salaam and Morogoro Campuses. The findings revealed that 94.7% of students were familiar with AI, mainly after beginning their studies; 87% used ChatGPT for learning, and 57% used AI to answer their questions. In addition, 98% of students argued that the utilisation of AI in ODL is inevitable, citing its role in enhancing self-learning, improving access to learning materials, and saving time. Based on the findings, the study suggests that enhanced access to and awareness of diverse AI tools may help maximise their potential benefits in learning. The study also calls for academic integrity, ethical use, peer learning, and human-AI interaction among ODL students and institutions for the effective utilisation of AI in ODL. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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27 pages, 2572 KB

Open AccessArticle

Automating Lexical Graph Construction with Large Language Models: A Scalable Approach to Japanese Multi-Relation Lexical Networks

by Benedikt Perak and Dragana Špica

Knowledge 2025, 5(4), 24; https://doi.org/10.3390/knowledge5040024 - 27 Oct 2025

Viewed by 2031

Abstract

In recent advancements within natural language processing (NLP), lexical networks play a crucial role in representing semantic relationships between words, enhancing applications from word sense disambiguation to educational tools. Traditional methods for constructing lexical networks, however, are resource-intensive, relying heavily on expert lexicographers. [...] Read more.

In recent advancements within natural language processing (NLP), lexical networks play a crucial role in representing semantic relationships between words, enhancing applications from word sense disambiguation to educational tools. Traditional methods for constructing lexical networks, however, are resource-intensive, relying heavily on expert lexicographers. Leveraging GPT-4o, a large language model (LLM), our study presents an automated, scalable approach to creating multi-relational Japanese lexical networks for the general Japanese language. This study builds on previous methods of integrating synonyms but extends to other relations such as hyponymy, hypernymy, meronymy, and holonomy. Using a combination of structured prompts and graph-based data storage, the model extracts detailed lexical relationships, which are then systematically validated and encoded. Results reveal a substantial expansion in network size, with over 155,000 nodes and 700,000 edges, enriching Japanese lexical associations with nuanced hierarchical and associative layers. Comparisons with WordNet show substantial alignment in relation types, particularly with soft matching, underscoring the model’s efficacy in reflecting the multifaceted nature of lexical semantics. This work contributes a versatile framework for constructing expansive lexical resources that hold promises for enhancing NLP tasks and educational applications across various languages and domains. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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32 pages, 781 KB

Open AccessArticle

Navigating Emotional Barriers and Cognitive Drivers in Mobile Learning Adoption Among Greek University Students

by Stefanos Balaskas, Vassilios Tsiantos, Sevaste Chatzifotiou, Dionysia Filiopoulou, Kyriakos Komis and George Androulakis

Knowledge 2025, 5(4), 23; https://doi.org/10.3390/knowledge5040023 - 11 Oct 2025

Cited by 1 | Viewed by 2001

Abstract

Mobile learning (m-learning) technologies are gaining popularity in universities but not uniformly across institutions because of cognitive, affective, and behavior obstacles. This research tested and applied an expansion of the Technology Acceptance Model (TAM) with technostress (TECH) and resistance to change (RTC) as [...] Read more.

Mobile learning (m-learning) technologies are gaining popularity in universities but not uniformly across institutions because of cognitive, affective, and behavior obstacles. This research tested and applied an expansion of the Technology Acceptance Model (TAM) with technostress (TECH) and resistance to change (RTC) as affective obstacles, as well as the core predictors of perceived usefulness (PU), perceived ease of use (PE), and perceived risk (PR). By employing a cross-sectional survey of Greek university students (N = 608) and partial least squares structural equation modeling (PLS-SEM), we tested direct and indirect impacts on behavioral intention (BI) to apply m-learning applications. The results affirm that PU and PE are direct predictors of BI, while PR has no direct impact on BI but acts indirectly through TECH and RTC. Mediation is partial in terms of PE and PU and indirect-only (complete) in terms of PR with respect to the impact of affective states on adoption. Multi-group comparisons found differences in terms of gender, age, confidence, and years of use but not frequency of use, implying that psychological and experiential characteristics have a greater impact on intention than habitual patterns. These results offer theory-driven and segment-specific guidelines for psychologically aware, user-focused m-learning adoption in higher education. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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14 pages, 2104 KB

Open AccessArticle

A Mathematical Model on Brain’s Ability of Learning

by Eleftherios Protopapas

Knowledge 2025, 5(3), 19; https://doi.org/10.3390/knowledge5030019 - 17 Sep 2025

Viewed by 1884

Abstract

The human brain is one of the most complex parts of the human body. Its function has been studied extensively in biology and medicine. Along this line, applied mathematics plays a crucial role through the formulation and analysis of mathematical models. A student’s [...] Read more.

The human brain is one of the most complex parts of the human body. Its function has been studied extensively in biology and medicine. Along this line, applied mathematics plays a crucial role through the formulation and analysis of mathematical models. A student’s ability to learn is an important aspect of these studies. In this paper, a theoretical mathematical model is presented to study the brain’s ability to learn, with parameters such as human intelligence, the expected amount of knowledge a student seeks to acquire, and the tendency to forget. A parametric study of the obtained model is conducted, and by taking into account actual data from the literature, the values of the parameters that fit these data are derived, demonstrating the validity of the model. The findings of this study indicate that the proposed model accurately embodies the core principles of mastery learning and offers a practical framework that educators can employ to improve instructional planning, thereby optimizing students’ readiness for examinations scheduled on fixed dates. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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25 pages, 5773 KB

Open AccessArticle

FEA-Assisted Test Bench to Enhance the Comprehension of Vibration Monitoring in Electrical Machines—A Practical Experiential Learning Case Study

by Jose E. Ruiz-Sarrio, Carlos Madariaga-Cifuentes and Jose A. Antonino-Daviu

Knowledge 2025, 5(3), 16; https://doi.org/10.3390/knowledge5030016 - 12 Aug 2025

Viewed by 1230

Abstract

Rotating electrical machine maintenance is a core component of engineering education curricula worldwide. Within this context, vibration monitoring represents a widespread methodology for electrical rotating machinery monitoring. However, the multi-physical nature of vibration monitoring presents a complex learning scenario, including concepts from both [...] Read more.

Rotating electrical machine maintenance is a core component of engineering education curricula worldwide. Within this context, vibration monitoring represents a widespread methodology for electrical rotating machinery monitoring. However, the multi-physical nature of vibration monitoring presents a complex learning scenario, including concepts from both mechanical and electrical engineering domains. This article proposes a novel knowledge-based educational experience design leveraging an integrated FEA-assisted test bench aimed at comprehensively addressing the electromechanical link between stator current and frame vibration. To this aim, a Finite Element Analysis (FEA) model is utilized to link excitation electrical signals with airgap radial forces acting in the stator. The subsequent correlation of these FEA predictions with measured frame vibrations on a physical test bench provides students with the theoretical concepts and practical tools to adequately comprehend this complex multi-physical phenomenon of wide application in real industrial scenarios. The pedagogical potential of the method also includes the development of critical thinking and problem-solving soft skills, and foundational understanding for digital twin concepts. A Delphi-style expert survey conducted with 25 specialists yielded strong support for the pedagogical robustness and relevance of the method, with mean ratings between 4.32 and 4.64 out of 5 across key dimensions. These results confirm the potential to enhance deep understanding and practical skills in vibration-based electrical machine diagnosis. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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17 pages, 1707 KB

Open AccessArticle

A Structural Causal Model Ontology Approach for Knowledge Discovery in Educational Admission Databases

by Bern Igoche Igoche, Olumuyiwa Matthew and Daniel Olabanji

Knowledge 2025, 5(3), 15; https://doi.org/10.3390/knowledge5030015 - 4 Aug 2025

Cited by 2 | Viewed by 2437

Abstract

Educational admission systems, particularly in developing countries, often suffer from opaque decision processes, unstructured data, and limited analytic insight. This study proposes a novel methodology that integrates structural causal models (SCMs), ontological modeling, and machine learning to uncover and apply interpretable knowledge from [...] Read more.

Educational admission systems, particularly in developing countries, often suffer from opaque decision processes, unstructured data, and limited analytic insight. This study proposes a novel methodology that integrates structural causal models (SCMs), ontological modeling, and machine learning to uncover and apply interpretable knowledge from an admission database. Using a dataset of 12,043 records from Benue State Polytechnic, Nigeria, we demonstrate this approach as a proof of concept by constructing a domain-specific SCM ontology, validate it using conditional independence testing (CIT), and extract features for predictive modeling. Five classifiers, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were evaluated using stratified 10-fold cross-validation. SVM and KNN achieved the highest classification accuracy (92%), with precision and recall scores exceeding 95% and 100%, respectively. Feature importance analysis revealed ‘mode of entry’ and ‘current qualification’ as key causal factors influencing admission decisions. This framework provides a reproducible pipeline that combines semantic representation and empirical validation, offering actionable insights for institutional decision-makers. Comparative benchmarking, ethical considerations, and model calibration are integrated to enhance methodological transparency. Limitations, including reliance on single-institution data, are acknowledged, and directions for generalizability and explainable AI are proposed. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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16 pages, 358 KB

Open AccessArticle

Artificial Intelligence in Curriculum Design: A Data-Driven Approach to Higher Education Innovation

by Thai Son Chu and Mahfuz Ashraf

Knowledge 2025, 5(3), 14; https://doi.org/10.3390/knowledge5030014 - 29 Jul 2025

Cited by 13 | Viewed by 10454

Abstract

This paper shows that artificial intelligence is fundamentally transforming college curricula by enabling data-driven personalization, which enhances student outcomes and better aligns educational programs with evolving workforce demands. Specifically, predictive analytics, machine learning algorithms, and natural language processing were applied here, grounded in [...] Read more.

This paper shows that artificial intelligence is fundamentally transforming college curricula by enabling data-driven personalization, which enhances student outcomes and better aligns educational programs with evolving workforce demands. Specifically, predictive analytics, machine learning algorithms, and natural language processing were applied here, grounded in constructivist learning theory and Human–Computer Interaction principles, to evaluate student performance and identify at-risk students to propose personalized learning pathways. Results indicated that the AI-based curriculum achieved much higher course completion rates (89.72%) as well as retention (91.44%) and dropout rates (4.98%) compared to the traditional model. Sentiment analysis of learner feedback showed a more positive learning experience, while regression and ANOVA analyses proved the impact of AI on enhancing academic performance to be real. Therefore, the learning content delivery for each student was continuously improved based on individual learner characteristics and industry trends by AI-enabled recommender systems and adaptive learning models. Its advantages notwithstanding, the study emphasizes the need to address ethical concerns, ensure data privacy safeguards, and mitigate algorithmic bias before an equitable outcome can be claimed. These findings can inform institutions aspiring to adopt AI-driven models for curriculum innovation to build a more dynamic, responsive, and learner-centered educational ecosystem. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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22 pages, 450 KB

Open AccessArticle

Ayatutu as a Framework for Mathematics Education: Integrating Indigenous Philosophy with Cooperative Learning Approaches

by Terungwa James Age

Knowledge 2025, 5(2), 11; https://doi.org/10.3390/knowledge5020011 - 9 Jun 2025

Cited by 3 | Viewed by 3204

Abstract

This article explores the integration of “Ayatutu”, a communal philosophy from Nigeria’s Tiv people, into mathematics education frameworks. Ayatutu—embodying collective responsibility and mutual assistance—aligns with contemporary cooperative learning approaches while offering unique cultural dimensions. Through analysis of the ethnomathematics literature, indigenous knowledge systems, [...] Read more.

This article explores the integration of “Ayatutu”, a communal philosophy from Nigeria’s Tiv people, into mathematics education frameworks. Ayatutu—embodying collective responsibility and mutual assistance—aligns with contemporary cooperative learning approaches while offering unique cultural dimensions. Through analysis of the ethnomathematics literature, indigenous knowledge systems, and cooperative learning theories this article develops a theoretical framework for Ayatutu-based mathematics instruction built on the following five core elements: collective problem-solving, resource sharing, complementary expertise, process orientation, and intergenerational knowledge transfer. The framework demonstrates significant alignment with sociocultural learning theory, communities of practice, and critical pedagogy while also offering potential benefits including enhanced mathematical engagement, positive identity development, stronger learning communities, and cultural sustainability. Implementation challenges involving teacher preparation, structural constraints, cultural translation, and balancing individual with collective learning are examined. This research contributes to decolonizing mathematics education by positioning indigenous philosophical systems as valuable resources for creating culturally responsive and mathematically powerful learning environments that serve diverse student populations while honoring cultural wisdom. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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15 pages, 1182 KB

Open AccessArticle

Interpretable Ensemble Learning Approach for Predicting Student Adaptability in Online Education Environments

by Shakib Sadat Shanto and Akinul Islam Jony

Knowledge 2025, 5(2), 10; https://doi.org/10.3390/knowledge5020010 - 3 Jun 2025

Cited by 5 | Viewed by 3111

Abstract

The COVID-19 pandemic has accelerated the shift towards online education, making it a critical focus for educational institutions. Understanding students’ adaptability to this new learning environment is crucial for ensuring their academic success. This study aims to predict students’ adaptability levels in online [...] Read more.

The COVID-19 pandemic has accelerated the shift towards online education, making it a critical focus for educational institutions. Understanding students’ adaptability to this new learning environment is crucial for ensuring their academic success. This study aims to predict students’ adaptability levels in online education using a dataset of 1205 observations that incorporates sociodemographic factors and information collected across different educational levels (school, college, and university). Various machine learning (ML) and deep learning (DL) models, including decision tree (DT), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), XGBoost, and artificial neural networks (ANNs), are applied for adaptability prediction. The proposed ensemble model achieves superior performance with 95.73% accuracy, significantly outperforming traditional ML and DL models. Furthermore, explainable AI (XAI) techniques, such as LIME and SHAP, were employed to uncover the specific features that significantly impact the adaptability level predictions, with financial condition, class duration, and network type emerging as key factors. By combining robust predictive modeling and interpretable AI, this study contributes to the ongoing efforts to enhance the effectiveness of online education and foster student success in the digital age. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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19 pages, 2058 KB

Open AccessArticle

CORE: Cultivation of Collaboration Skills via Educational Robotics

by Emmanouil A. Demetroulis, Ilias Papadogiannis, Manolis Wallace, Vassilis Poulopoulos and Angeliki Antoniou

Knowledge 2025, 5(2), 9; https://doi.org/10.3390/knowledge5020009 - 6 May 2025

Cited by 3 | Viewed by 4236

Abstract

Collaboration skills are an important component of 21st century skills and a critical skill for citizens of the future. In this work, we propose collaboration-oriented robotics education (CORE), a methodology aimed at fostering the development of collaboration skills in primary school students aged [...] Read more.

Collaboration skills are an important component of 21st century skills and a critical skill for citizens of the future. In this work, we propose collaboration-oriented robotics education (CORE), a methodology aimed at fostering the development of collaboration skills in primary school students aged 11–12 via an adjusted approach to the teaching of educational robotics. In order to assess the existence and level of collaboration skills in a student, a suitable tool is also proposed. Using a collaboration-oriented performance evaluation test (COPE) for both a pre- and post-intervention measurement and applying both the conventional and CORE approaches to teaching educational robotics to 32 students, split into control and intervention groups, we demonstrate the effectiveness of the proposed approach. Specifically, the experimental implementation shows that CORE statistically significantly increases the performance of the experimental group compared to the conventional way of teaching educational robotics. These results, in addition to validating CORE itself, demonstrate that the conventional approach to STEAM (Science, Technology, Engineering, Arts, Mathematics) education is not necessarily already optimized, thus facilitating an overall re-evaluation of the field. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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Review

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34 pages, 1183 KB

Open AccessReview

Generative AI as a Sociotechnical Challenge: Inclusive Teaching Strategies at a Hispanic-Serving Institution

by Víctor D. Carmona-Galindo, Hou Ung, Manhao Zeng, Christine Broussard, Elizaveta Taranenko, Yousef Daneshbod, David Chappell and Todd Lorenz

Knowledge 2025, 5(3), 18; https://doi.org/10.3390/knowledge5030018 - 10 Sep 2025

Cited by 3 | Viewed by 2796

Abstract

Generative artificial intelligence (GenAI) is reshaping science, technology, engineering, and mathematics (STEM) education by offering new strategies to address persistent challenges in equity, access, and instructional capacity—particularly within Hispanic-Serving Institutions (HSIs). This review documents a faculty-led, interdisciplinary initiative at the University of La [...] Read more.

Generative artificial intelligence (GenAI) is reshaping science, technology, engineering, and mathematics (STEM) education by offering new strategies to address persistent challenges in equity, access, and instructional capacity—particularly within Hispanic-Serving Institutions (HSIs). This review documents a faculty-led, interdisciplinary initiative at the University of La Verne (ULV), an HSI in Southern California, to explore GenAI’s integration across biology, chemistry, mathematics, and physics. Adopting an exploratory qualitative design, this study synthesizes faculty-authored vignettes with peer-reviewed literature to examine how GenAI is being piloted as a scaffold for inclusive pedagogy. Across disciplines, faculty-reported benefits such as simplifying complex content, enhancing multilingual comprehension, and expanding access to early-stage research and technical writing. At the same time, limitations—including factual inaccuracies, algorithmic bias, and student over-reliance—underscore the importance of embedding critical AI literacy and ethical reflection into instruction. The findings highlight equity-driven strategies that position GenAI as a complement, not a substitute, for disciplinary expertise and culturally responsive pedagogy. By documenting diverse, practice-based applications, this review provides a flexible framework for integrating GenAI ethically and inclusively into undergraduate STEM instruction. The insights extend beyond HSIs, offering actionable pathways for other minority-serving and resource-constrained institutions. Full article

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Other

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28 pages, 6985 KB

Open AccessSystematic Review

Unveiling the Unspoken: A Conceptual Framework for AI-Enabled Tacit Knowledge Co-Evolution

by Nasser Khalili and Mohammad Jahanbakht

Knowledge 2026, 6(1), 1; https://doi.org/10.3390/knowledge6010001 - 23 Dec 2025

Cited by 2 | Viewed by 5335

Abstract

This study conducts a systematic bibliometric review of artificial intelligence (AI)-based approaches to tacit knowledge extraction and management. Drawing on data retrieved from Scopus and Web of Science, this study analyzes 126 publications published between 1985 and 2025 using VOSviewer and Biblioshiny to [...] Read more.

This study conducts a systematic bibliometric review of artificial intelligence (AI)-based approaches to tacit knowledge extraction and management. Drawing on data retrieved from Scopus and Web of Science, this study analyzes 126 publications published between 1985 and 2025 using VOSviewer and Biblioshiny to map citation networks, keyword co-occurrence patterns, and thematic evolution. The results identify nine major clusters spanning machine learning, natural language processing, semantic modeling, expert systems, knowledge-based decision support, and emerging hybrid techniques. Collectively, these findings indicate a field-wide shift from manual codification toward scalable, context-aware, and semantically enriched approaches that better support tacit knowing in organizational practice. Building on these insights, the paper introduces the AI–Tacit Knowledge Co-Evolution Model, which situates AI as an epistemic partner—augmenting human interpretive processes rather than merely codifying experience. The framework integrates Polanyi’s concept of tacit knowing, Nonaka’s SECI model, and sociotechnical learning theories to elucidate how human–AI interaction transforms the dynamics of knowledge creation. The review consolidates fragmented research streams and provides a conceptual foundation for guiding future methodological development in AI-enabled tacit knowledge management. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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