Publications

Abstract

This Work in Progress paper examines the classroom implementation of a low-cost 3D printed learning kit designed to enhance CAD proficiency, engagement, problem solving, and application of mechanical design concepts. The learning kit supports measurement-based modeling, material assignment, mass property verification, open-ended redesign, and optional interdisciplinary exploration. This study evaluates how use of the learning kit influences engagement, perceived CAD learning, confidence, and performance differentiation. Evidence includes an eleven item five point Likert survey, qualitative student reflections, learning kit scores, CSWA outcomes, and comparison of final grade distributions between semesters with and without the learning kit. Findings indicate increased engagement, positive perceived learning gains, and meaningful differentiation in performance without evidence of grade inflation. The paper also discusses logistical considerations, collaboration dynamics, and future refinements.

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BibTeX

@article{2026wip-a-cost-effective-hands-on-learni,
  title = {WIP: A Cost-Effective Hands-On Learning Kit for Enhancing Engagement and Design Skills},
  author = {Sayginer, Osman and Riggio, Laura and Pillapakkam, Alex},
  year = {2026},
  journal = {2026 ASEE Annual Conference & Exposition}
}

Abstract

A senior level Mechatronics Design course in the Department of Mechanical Engineering was launched in Fall 2025 and organized around the 2026 ASME Student Design Competition. Because students worked on distinct subsystems within open ended team projects, consistent formative evaluation of individual understanding was challenging. To support this need, an AI assisted “AI Chatbot Quiz” was implemented using a custom chatbot built on the Google Gemini API and guided by course materials, student project proposals, and competition rules. During a timed conversational quiz, the chatbot asked probing questions, provided supportive hints when needed, and encouraged reflection on design decisions. Chat transcripts were submitted for review, and six students completed an immediate post quiz survey. Results suggest the format was engaging and supported critical thinking, while student comfort and preference relative to traditional evaluations were mixed. Lessons learned highlight the feasibility of AI assisted conversational evaluation in design courses, along with practical needs for clear expectations, subsystem alignment, and transparency in grading.

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BibTeX

@article{2026ttt-ai-in-teaching-evaluating-open-e,
  title = {TT&T: AI in Teaching: Evaluating Open-Ended Projects Using an AI Chatbot Quiz},
  author = {Sayginer, Osman},
  year = {2026},
  journal = {2026 ASEE Annual Conference & Exposition}
}

Abstract

A low-cost, 3D-printed learning kit was deployed during Fall 2025 in MEE 117 Fundamentals of Mechanical Engineering Design. The study examines CAD practice, open-ended problem solving, and multidisciplinary integration. Surveys and graded assignments document in-class engagement, modeling accuracy, and approaches to data-informed design reasoning without reporting results or interpretations.

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BibTeX

@posterpresentation{2026classroom-kit-for-hands-on-engineeri,
  title = {Classroom Kit for Hands-On Engineering and Multidisciplinary Integration: Assessing Student Engagement and Learning Contribution},
  author = {Sayginer, Osman and Kim Doyoung},
  year = {2026},
  journal = {24th Annual Faculty Conference on Teaching Excellence}
}

Abstract

Many new engineering students don’t see the connections between many of their classes. Integrating curriculum can not only help students see the connections, but can also lead to increased retention of those students. It is also much easier to implement integrated curricula in the first year of engineering program as there are a common set of courses, but it becomes more difficult to integrate freshman-junior level coursework. This paper explores an idea that will allow this integration. This work introduces a custom-designed, cost-effective learning kit that fosters hands-on classroom activities, peer collaboration, and interdisciplinary learning, bridging concepts from freshman to junior-level coursework. The accessibility of low-cost electronic components and rapid prototyping techniques, such as 3D printing makes this integration possible.

We demonstrate a small-scale 3D-printed wind turbine that can be assembled using inexpensive mechanical components such as bearings, nuts, and screws. This hands-on tool is integrated into MEE 1117 Fundamentals of Mechanical Engineering Design, where students disassemble, and measure turbine components using calipers and replicate their models in SolidWorks. This activity strengthens fundamental CAD skills while reinforcing mechanical design principles.

Simultaneously, students enrolled in ENGR 1101 Introduction to Engineering leverage Microbit electronics kits to explore programming fundamentals. By utilizing the Microbit’s built-in sensors, specifically touch sensors and photodiodes, students measure the turbine's RPM. This real-world data collection introduces key instrumentation concepts relevant to MEE 2305 Instrumentation and Data Acquisition Lab and ENGR 2332 Dynamics. Further customization of the setup enables students to explore gear ratios and torque calculations, forming a foundation for MEE 3301 Machine Theory and Design.

This interdisciplinary approach provides first-year students with a tangible, real-world problem to solve, reinforcing fundamental engineering concepts through active learning. By integrating cost-effective prototyping, we enhance engagement, problem-solving skills, and a deeper understanding of mechanical and electrical engineering principles early in the academic journey which can feed into other benefits for our students such as retention.

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BibTeX

@inproceedings{sayginer2025gifts,
  title = {GIFTS: Bridging Engineering Education with a Cost-Effective Classroom Kit: A Hands-On Approach to Active Learning},
  author = {Sayginer, Osman and Budischak, Cory and Riggio, Laura and others},
  year = {2025},
  journal = {FYEE 2025 Conference},
  doi = {10.18260/1-2--55263}
}

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BibTeX

@inproceedings{tran2025flexible,
  title = {Flexible glass planar structures fabricated by rf-sputtering},
  author = {Tran, LTN and Lis, S Sudha Maria and Sengottaiyan, R and Carlotto, A and Szczurek, A and Dell'Anna, R and Babiarczu, B and Sayginer, O and Varas, S and Vinante, A and others},
  year = {2025},
  journal = {EPJ Web of Conferences},
  volume = {335},
  pages = {08006},
  doi = {10.1051/epjconf/202533508006},
  issn = {2100-014X}
}

Abstract

The rapid advancements in artificial intelligence (AI) are reshaping numerous fields, with education being a key beneficiary. Generative AI, known for its capability in reasoning and content creation, presents an innovative approach to enhancing digital learning environments. This study explores an automated methodology for generating quiz questions using AI-powered tools in conjunction with the Question and Test Interoperability (QTI) format. By integrating Generative AI with widely used teaching content management systems such as Canvas and Moodle, we demonstrate how AI-driven automation streamlines the creation of diverse assessment formats, including multiple-choice, fill-in-the-blank, and numeric/text input questions.

To implement this approach, we first define structured input formats that guide the AI in generating quiz questions. We employ prompt engineering techniques to instruct the AI on the desired question complexity, format, and alignment with learning objectives.

More specifically our approach is to feed a generative AI system the QTI format that we need output and feed it our learning goals in some instances or sample questions in others and ask it to generate (in QTI format) other questions. Then we review these questions for accuracy. We also explore how different prompting may affect the quality of the generated questions. For example, what if our prompt includes a website or pdf of evidence based guides for creating test questions such as <em>Writing Test Items To Evaluate Higher Order Thinking</em> by Thomas Haladyna. We explore whether this prompting strategy creates questions that better assess student learning.

Our approach significantly reduces the time and effort required for educators to design assessments while ensuring variability, adaptability, and quality in quiz content. The system enables dynamic question generation, allowing for real-time adjustments to difficulty levels, and question structures. Additionally, the use of standardized QTI format facilitates seamless integration across different learning management platforms, ensuring interoperability and broad applicability.

By leveraging the capabilities of AI, this method enhances both formative and summative assessment strategies, improving the overall efficiency of teaching, testing, and student evaluation processes. This efficiency leaves more time for the human to human interaction that is so essential to student motivation and learning. This work highlights the transformative potential of AI-powered quiz generation in educational technology. Utilizing this technology can pave the way for future work. One example is scaling this technique with different prompting strategies such as prompting AI with a full syllabus. Another is using the data from these assessments to automatically generate follow up questions that adapt to that particular student. The foundational techniques described in this paper are key to these long term objectives.

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BibTeX

@inproceedings{sayginer2025ai2qti,
  title = {GIFTS: AI2QTI: Automated Quiz Generation Using Generative AI and QTI for Teaching Content Management Systems},
  author = {Sayginer, Osman and Budischak, Cory},
  year = {2025},
  journal = {FYEE 2025 Conference},
  doi = {10.18260/1-2--55259}
}

Abstract

Generative AI is reshaping traditional education by transforming teaching into AI-based learning, where AI acts as both a resource and a personalized tutor. It offers faster access to information, providing students with tailored explanations from diverse perspectives. While this fosters interdisciplinary collaboration and holistic problem-solving, it also requires careful attention to the accuracy of information. In a case study, I grouped students from different academic backgrounds, where they used AI to generate personalized research topics that aligned with their majors and interests. These individual topics contributed to a collective solution for a broader problem, showcasing GenAI's potential in fostering curiosity-driven research.

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BibTeX

@lightningtalk{2025how-can-genai-foster-interdisciplina,
  title = {How Can GenAI Foster Interdisciplinary Research on Open-Ended Problems Among Undergraduates?},
  author = {Sayginer, Osman},
  year = {2025},
  journal = {23rd Annual Faculty Conference on Teaching Excellence}
}

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BibTeX

@inproceedings{zanetti20241d,
  title = {1D photonic crystals fabricated by RF sputtering},
  author = {Zanetti, Giacomo and Carlotto, Alice and Tran, LTN and Szczurek, Anna and Babiarczuk, Bartosz and Sayginer, Osman and Varas, Stefano and Vinante, Andrea and Krzak, Justyna and Bursi, Oreste S and others},
  year = {2024},
  journal = {Fiber Lasers and Glass Photonics: Materials through Applications IV},
  volume = {13003},
  pages = {126--137},
  doi = {10.1117/12.3025887}
}

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BibTeX

@article{zanetti2023under,
  title = {Under bending optical assessment of flexible glass based multilayer structures fabricated on polymeric substrates},
  author = {Zanetti, Giacomo and Carlotto, Alice and Tran, Thi Ngoc Lam and Szczurek, Anna and Babiarczuk, Bartosz and Sayginer, Osman and Varas, Stefano and Krzak, Justyna and Bursi, Oreste and Zonta, Daniele and others},
  year = {2023},
  journal = {Optical Materials: X},
  volume = {19},
  pages = {100241},
  doi = {10.1016/j.omx.2023.100241},
  issn = {2590-1478}
}

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BibTeX

@inproceedings{carlotto2023multi,
  title = {Multi-Cavity Dielectric Mirrors for Spectral-Splitting Photovoltaic Applications},
  author = {Carlotto, Alice and Chiasera, Alessandro and Ferrari, Maurizio and Varas, Stefano and Zanetti, Giacomo and Sayginer, Osman and Bonomo, Matteo and Galliano, Simone and Barolo, Claudia and Farina, Andrea and others},
  year = {2023},
  journal = {2023 Photonics North (PN)},
  pages = {1--3},
  doi = {10.1109/pn58661.2023.10223005}
}