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AI and Sensors in Computer-Based Educational Systems
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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 1517

Special Issue Editor

Dr. Ahmet Can Sabuncu

E-Mail Website
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 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-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 (3 papers)

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Research

24 pages, 2676 KB  
Article
The Adaptive Lab Mentor (ALM): An AI-Driven IoT Framework for Real-Time Personalized Guidance in Hands-On Engineering Education
by Md Shakib Hasan, Awais Ahmed, Nouman Rasool, MST Mosaddeka Naher Jabe, Xiaoyang Zeng and Farman Ali Pirzado
Sensors 2025, 25(24), 7688; https://doi.org/10.3390/s25247688 - 18 Dec 2025
Abstract
Engineering education is based on experiential learning, but the problem is that in laboratory conditions, it is difficult to give feedback to the students in real time and personalize this feedback. The paper introduces the proposal of an innovative approach to the laboratories, [...] Read more.
Engineering education is based on experiential learning, but the problem is that in laboratory conditions, it is difficult to give feedback to the students in real time and personalize this feedback. The paper introduces the proposal of an innovative approach to the laboratories, called Adaptive Lab Mentor (ALM), which combines the technologies of Artificial Intelligence (AI), Internet of Things (IoT), and sensor technology to facilitate intelligent and customized laboratory setting. ALM is supported by a new real-time multimodal sensor fusion model in which a sensor-instrumented laboratory is used to record real-time electrical measurements (voltage and current) which are used in parallel with symbolic component measurements (target resistance) with a lightweight, dual-input Convolutional Neural Network (1D-CNN) running on an edge device. In this initial validation, visual context is presented as a symbolic target value, which establishes a pathway for the future integration of full computer vision. The architecture will enable monitoring of the student progress, making error diagnoses within a short time period, and provision of adaptive feedback based on information available in the context. To test this strategy, a high-fidelity model of an Ohm Laboratory was developed. LTspice was used to generate a huge amount of current and voltage time series of various circuit states. The trained model achieved 93.3% test accuracy and demonstrated that the proposed system could be applied. The ALM model, compared to the current Intelligent Tutoring Systems, is based on physical sensing and edge AI inference in real-time, as well as adaptive and safety-sensitive feedback throughout hands-on engineering demonstrations. The ALM framework serves as a blueprint for the new smart laboratory assistant. Full article
(This article belongs to the Special Issue AI and Sensors in Computer-Based Educational Systems)
37 pages, 6306 KB  
Article
Enhancing Computational Thinking and Programming Logic Skills with App Inventor 2 and Robotics: Effects on Learning Outcomes, Motivation, and Cognitive Load
by Yu-Ting Huang, Chien-Lung Li, Chin-Chih Chang and Wernhuar Tarng
Sensors 2025, 25(22), 7059; https://doi.org/10.3390/s25227059 - 19 Nov 2025
Viewed by 526
Abstract
Educational robotics (ER) has attracted growing attention as an effective means of cultivating computational thinking and programming skills through interactive, sensor-based learning environments. Integrating ER with visual programming platforms enables learners to engage in hands-on, technology-driven problem solving within authentic contexts. This study [...] Read more.
Educational robotics (ER) has attracted growing attention as an effective means of cultivating computational thinking and programming skills through interactive, sensor-based learning environments. Integrating ER with visual programming platforms enables learners to engage in hands-on, technology-driven problem solving within authentic contexts. This study aimed to investigate the effects of a task-oriented instructional module, grounded in constructivist and experiential learning theories, that integrated App Inventor 2 with a six-axis robotic arm on junior high school students’ learning performance. A quasi-experimental design was conducted with 74 eighth-grade students from a junior high school in Hsinchu, Taiwan. The experimental group (n = 37) engaged in hands-on programming and robotic arm operations, whereas the control group (n = 37) received equivalent programming instruction with video demonstrations. Results indicated that the experimental group achieved significantly higher scores in spatial understanding, computational thinking, and programming logic. Students also reported greater motivation, lower cognitive load, and higher satisfaction with the integrated system. These findings suggest that combining App Inventor 2 with a physical robotic arm provides an effective framework for promoting computational thinking, motivation, and system interaction in technology education and smart learning environments. Full article
(This article belongs to the Special Issue AI and Sensors in Computer-Based Educational Systems)
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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 427
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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