Artificial Intelligence in Engineering Education
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 June 2026 | Viewed by 838
Special Issue Editors
Interests: learning models; continual learning models; engineering education; AI-enhanced curriculum design; students’ success; program assessment
Interests: intelligent fault detection and recovery; condition based monitoring, reliability; manufacturing systems; robotics; VR/RL based failure analysis
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence
Interests: knowledge management; enterprise architecture; IS success; ethical AI & trust; human-centric design; innovation; curriculum development
Special Issue Information
Dear Colleagues,
This Special Issue examines the growing role of Artificial Intelligence (AI) in transforming engineering education. We seek contributions that address how AI tools, methods, and computational frameworks can enhance instructional design, enable personalized and adaptive learning, and optimize data-driven assessment and decision -making in engineering programs.
Topics of interest include (but are not limited to) AI-driven adaptive and intelligent tutoring systems, generative AI for engineering problem-solving and automated content generation, simulation-based and virtual laboratories, data analytics for learning performance modeling, curriculum redesign for AI literacy and competency alignment, and approaches that promote innovation and technical skill development for the future engineering workforce.
We particularly welcome work that evaluates the performance and scalability of AI-enhanced learning environments, investigates algorithmic models for knowledge tracing and student modeling, and analyzes the integration of AI technologies into core engineering courses and laboratory experiences. Both theoretical and applied research are encouraged, as well as implementation case studies demonstrating measurable learning outcomes and instructional advancements.
By gathering interdisciplinary perspectives, this Special Issue aims to showcase how AI can advance engineering pedagogy, assessment, and instructional design while redefining the computational infrastructure supporting engineering education.
Dr. Ghazal Barari
Dr. Kouroush Jenab
Dr. Omar Ochoa
Dr. Leila Halawi
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-blind 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
- adaptive and intelligent tutoring systems
- generative AI for engineering problem solving
- AI-enhanced simulation and virtual laboratories
- learning analytics and educational data mining
- AI-driven workforce readiness and employability skills
- AI-enabled instructional design
- personalized and adaptive learning using AI
- AI-driven assessment and feedback
- engineering curriculum redesign for AI literacy
- scalable AI-enhanced learning environments
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