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AI and Sensors in Computer-Based Educational Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 705

Special Issue Editor


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Guest Editor
Worcester Polytechnic Institute, Worcester, MA 01609, USA
Interests: engineering education focuses on development of online laboratory exercises and the use of the Internet of Things and artificial intelligence in engineering education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will explore innovative applications of AI, IoT, and sensor technologies in regard to advancing educational systems, with a focus on addressing critical challenges in modern learning environments. The themes include the following:

Inclusive and Personalized Learning:

We invite studies that apply AI in sensor-rich educational environments to enable personalized learning pathways and inclusive systems that support learners with diverse needs. This includes contexts such as experimentation and instrumentation courses, as well as project-based learning environments where sensors are integral to the learning process.

Data-Driven Educational Systems:

Highlighting the use of IoT sensors and AI to collect, analyze, and utilize educational data to improve teaching strategies, student engagement, and institutional decision-making.

Troubleshooting and Diagnostic Training:

Examining how AI and sensor-enabled systems can enhance training for troubleshooting complex systems, with a focus on real-world scenarios and interdisciplinary problem-solving.

Experiential Learning with Sensors:

Exploring hands-on, sensor-based learning approaches that bridge theoretical concepts and practical applications in STEM and beyond.

Remote Laboratories:

Showcasing the role of IoT sensors and AI in creating remote lab experiences that enable students to access high-quality, hands-on education anywhere at any time.

Dr. Ahmet Can Sabuncu
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors 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 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

  • integration of AI and sensors
  • IoT sensors
  • AI and sensor-enabled systems

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Published Papers (1 paper)

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Research

19 pages, 1311 KB  
Article
An Interpretable Soft-Sensor Framework for Dissertation Peer Review Using BERT
by Meng Wang, Jincheng Su, Zhide Chen, Wencheng Yang and Xu Yang
Sensors 2025, 25(20), 6411; https://doi.org/10.3390/s25206411 - 17 Oct 2025
Viewed by 267
Abstract
Graduate education has entered the era of big data, and systematic analysis of dissertation evaluations has become crucial for quality monitoring. However, the complexity and subjectivity inherent in peer-review texts pose significant challenges for automated analysis. While natural language processing (NLP) offers potential [...] Read more.
Graduate education has entered the era of big data, and systematic analysis of dissertation evaluations has become crucial for quality monitoring. However, the complexity and subjectivity inherent in peer-review texts pose significant challenges for automated analysis. While natural language processing (NLP) offers potential solutions, most existing methods fail to adequately capture nuanced disciplinary criteria or provide interpretable inferences for educators. Inspired by soft-sensor, this study employs a BERT-based model enhanced with additional attention mechanisms to quantify latent evaluation dimensions from dissertation reviews. The framework integrates Shapley Additive exPlanations (SHAP) to ensure the interpretability of model predictions, combining deep semantic modeling with SHAP to quantify characteristic importance in academic evaluation. The experimental results demonstrate that the implemented model outperforms baseline methods in accuracy, precision, recall, and F1-score. Furthermore, its interpretability mechanism reveals key evaluation dimensions experts prioritize during the paper assessment. This analytical framework establishes an interpretable soft-sensor paradigm that bridges NLP with substantive review principles, providing actionable insights for enhancing dissertation improvement strategies. Full article
(This article belongs to the Special Issue AI and Sensors in Computer-Based Educational Systems)
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