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New Insights in Artificial Intelligence and E-Learning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 February 2027 | Viewed by 2531

Editors


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Guest Editor
Department of Computer Science Education, Seoul National University of Education, Seoul, Republic of Korea
Interests: information education; artificial intelligence education; information ethics; digital divide
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Departamento de Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Ctra. Madrid-Barcelona Km. 33.6, 28805 Alcalá de Henares, Madrid, Spain
Interests: soft computing; evolutionary algorithms; artificial intelligence; machine learning

Special Issue Information

Dear Colleagues,

In the modern information society, various information and communication technologies are used to enrich our day-to-day living, yet recently, the development and dissemination of various artificial intelligence technologies is causing further changes to our lives, particularly in the field of education. More specifically, the application of various artificial intelligence technologies in the field of e-learning is gradually raising the status of e-learning to a level equivalent to that of traditional education. In order to properly utilize artificial intelligence in e-learning, a basic understanding of artificial intelligence is required across both private and public education. Meanwhile, the application of artificial intelligence to e-learning also has its limitations, and comes with a range of challenges. It can be concluded that the emergence of artificial intelligence (AI) in education is reshaping teaching and learning models, particularly through digital environments. Consequently, this Special Issue aims to gather together research that critically examines the role of AI in education, with a strong emphasis on its ethical, social, and pedagogical implications. Contributions on topics such as AI literacy, intelligent tutoring systems, adaptive learning, and education in data science, programming, and algorithms are particularly welcome. Special attention will be given to studies addressing the ethics of AI in educational contexts, including algorithmic fairness, data privacy, transparency in automated systems, and the impact on student autonomy. This Special Issue seeks to promote an interdisciplinary approach to understanding both the opportunities and challenges of AI in e-learning, fostering academic dialog and offering guidance for the ethical and effective implementation of AI in digital education.

This Special Issue deals with various topics related to the use of artificial intelligence knowledge and technology in the field of e-learning. Areas of interest include, but are not limited to, the following representative topics:

-Artificial intelligence literacy;

-AI-based tutoring;

-Artificial intelligence ethics;

-AI-based adaptive learning;

-Application of artificial intelligence in education;

-Artificial intelligence convergence education;

-AI-based automatic scoring system;

-Data science education;

-Programming education;

-Algorithm education.

Dr. Woochun Jun
Dr. Pilar García-Díaz
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 single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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
  • E-learning
  • artificial intelligence applications
  • artificial intelligence literarcy
  • artifical intelligence ethics
  • data science education
  • AI-based tutoring

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Published Papers (3 papers)

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Research

17 pages, 1460 KB  
Article
A Curriculum-Embedded Two-Session AI Chatbot-Based History-Taking Practicum in Korean Medicine Diagnostics
by In-Young Choi, Jundong Kim, Ji-Hwan Kim, Hye-Yoon Lee, Won-Hwan Park, Chang-Eop Kim and Dong-Woo Lim
Appl. Sci. 2026, 16(14), 7223; https://doi.org/10.3390/app16147223 - 19 Jul 2026
Viewed by 320
Abstract
Background: History-taking is a core clinical competency in Korean medicine diagnostics, but conventional training methods such as peer role-play and standardized patient-based education have limitations in providing repeated, individualized, and scalable practice opportunities. This study aimed to evaluate the feasibility and educational value [...] Read more.
Background: History-taking is a core clinical competency in Korean medicine diagnostics, but conventional training methods such as peer role-play and standardized patient-based education have limitations in providing repeated, individualized, and scalable practice opportunities. This study aimed to evaluate the feasibility and educational value of a two-session AI chatbot-based history-taking practicum with automated feedback in Korean medicine diagnostics. Methods: This prospective single-arm repeated-measures educational study was conducted with fourth-year students at the College of Korean Medicine, Dongguk University, in May and June 2026. A total of 76 students participated in two chatbot-assisted history-taking sessions using dizziness and shoulder pain scenarios. Students completed surveys on baseline AI familiarity, chatbot experience, usability, and self-efficacy. Self-efficacy was assessed at three time points: before the first session, after the first session, and after the second session. Chatbot-generated feedback scores were compared between session 1 and 2 for each scenario using paired complete-case analyses. Open-ended responses were descriptively categorized. Results: Students rated the chatbot-based practicum positively in terms of active participation, perceived usefulness, accessibility, and convenience. Item-level self-efficacy analysis showed significant time effects in two domains: planning the conversation, and closing the conversation appropriately. The overall mean self-efficacy score gradually increased from 3.739 ± 0.546 before the first session to 3.887 ± 0.591 after the second session; however, the overall time effect did not reach statistical significance. Chatbot-generated feedback scores showed scenario-dependent patterns. Scores for the shoulder pain scenario increased from session 1 to session 2 before adjustment, but this change did not remain significant after Holm correction; scores for the dizziness scenario showed a non-significant decreasing trend. Open-ended responses indicated that students valued repeated practice and immediate feedback, while also noting limitations related to feedback accuracy, realism of patient responses, and the lack of physical examination or multimodal diagnostic information. Conclusions: The chatbot-assisted practicum was feasible and favorably perceived by students, with selected item-level changes and a modest non-significant upward trend in overall self-efficacy in this exploratory educational study. These findings support the potential role of AI chatbot-based simulation as a supplementary, scalable tool for repeated history-taking practice and formative feedback, rather than as a replacement for performance-based clinical skills training. Given the single-arm design and reliance on learner-reported outcomes, these findings should be interpreted as exploratory. Future controlled studies should incorporate objective performance outcomes, expert-validated automated scoring, non-AI comparison groups, and more realistic multimodal clinical scenarios to determine the educational effectiveness of chatbot-assisted history-taking training. Full article
(This article belongs to the Special Issue New Insights in Artificial Intelligence and E-Learning)
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22 pages, 5316 KB  
Article
Human-in-the-Loop AI Feedback in Interpreter Training: An ASR-Based Platform Analysis of Instructor Annotations, Comment Functions, and System Constraints
by Juriae Lee
Appl. Sci. 2026, 16(14), 7086; https://doi.org/10.3390/app16147086 - 15 Jul 2026
Viewed by 285
Abstract
The use of AI-assisted feedback systems has steadily increased in higher education, yet there has been limited research as to which components of expert feedback can be supported by AI and which require continued human judgment. This issue is particularly important in professional [...] Read more.
The use of AI-assisted feedback systems has steadily increased in higher education, yet there has been limited research as to which components of expert feedback can be supported by AI and which require continued human judgment. This issue is particularly important in professional training domains where feedback must rely upon contextual interpretation, source–output comparison, and domain expertise. Accordingly, this study analyzes instructor-generated feedback in TalkTrack, an automatic speech recognition (ASR)-supported digital platform for interpreter training, as a basis for human-in-the-loop AI feedback design. The dataset consists of 44 Korean–Japanese, Japanese–Korean, and Chinese–Korean interpreting performances produced by 12 graduate students and includes 931 feedback tags and 756 substantive qualitative comments manually assigned by 10 instructors. The categories most frequently assigned were expression and translation-error feedback, while corrective feedback was the dominant instructional function. Further, feedback behavior varied substantially by instructor and feedback category, indicating that different types of feedback require different levels of human intervention and system support. Accordingly, this study proposes a human-in-the-loop feedback architecture employing ASR and large language models to support detection, organization, retrieval, and suggestion generation, while instructors retain responsibility for source-text fidelity, discourse coherence, pedagogical judgment, and final feedback validation, from which this study proposes empirical design requirements for scalable AI-supported feedback systems in professional e-learning environments. Full article
(This article belongs to the Special Issue New Insights in Artificial Intelligence and E-Learning)
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10 pages, 343 KB  
Article
Promoting Academic Integrity in AI-Practice—The Effect of Live Coaching in Higher Education
by Renske Emicke and Claudia Kemper
Appl. Sci. 2026, 16(4), 2022; https://doi.org/10.3390/app16042022 - 18 Feb 2026
Cited by 1 | Viewed by 1265
Abstract
The rapid spread of generative artificial intelligence (AI) in higher education creates both opportunities for innovation and challenges for academic integrity, ethical use, and students’ critical thinking, particularly in scientific writing. This study examines whether a synchronous live coaching format can support students [...] Read more.
The rapid spread of generative artificial intelligence (AI) in higher education creates both opportunities for innovation and challenges for academic integrity, ethical use, and students’ critical thinking, particularly in scientific writing. This study examines whether a synchronous live coaching format can support students in developing reflective and responsible AI practices. A mixed-methods cross-sectional evaluation was conducted at a German distance-learning university with a strong focus on health and social sciences. An online survey was administered to 168 students who participated in voluntary live coaching sessions on “AI in Scientific Writing”. Quantitative items assessed perceived competence gains, ethical awareness, and confidence in handling AI tools, while open-ended questions captured qualitative feedback on the format’s strengths and improvement needs. Students reported that the coaching enhanced their understanding of responsible AI use and scientific integrity and valued the opportunity for open discussion, peer interaction, and the supportive attitude of instructors. Reflective and dialogic elements were perceived as particularly beneficial. Overall, the findings suggest that synchronous live coaching can contribute to fostering ethical awareness and higher-order thinking in AI-supported academic work, especially when it integrates structured input with dialogue, reflection, and peer learning. Full article
(This article belongs to the Special Issue New Insights in Artificial Intelligence and E-Learning)
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