Information Package / Course Catalogue
Artificial Intelligence-Supported Project Development
Course Code: MTE537
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

The objective of this course is to equip students with the ability to effectively integrate artificial intelligence technologies into the teaching of mathematics, and to develop their competencies in designing, developing, and evaluating innovative, pedagogically rich, and problem-solving-focused projects using these technologies.

Course Content

As part of the “AI-Supported Project Development” course, the role of artificial intelligence in education, its fundamental concepts, and current applications of these technologies in mathematics instruction are first addressed at a theoretical level. Throughout the course, students gain hands-on experience with various AI tools that offer data analysis, personalized learning, and intelligent feedback systems, enabling them to develop student-centered and innovative project designs aligned with the mathematics curriculum. In addition to technological applications, the ethical responsibilities associated with the use of artificial intelligence in education, data privacy regulations, and the limitations of these systems are discussed from a critical perspective. By the end of the course, students gain the ability to practically analyze the instructional effectiveness and assessment methods of their original interdisciplinary projects by presenting them.

Name of Lecturer(s)
Learning Outcomes
1.It turns a project idea into a product.
2.Prepares work in a manner consistent with the ethical use of artificial intelligence in the project development process.
3.Develops an original project aimed at filling a gap in the literature in accordance with the principles of academic writing.
4.Prepares a report on the project proposal in accordance with academic writing guidelines.
5.During the project development process, they identify the AI tool that best suits their needs.
Recommended or Required Reading
1.Khan, S. (2024). Brave new words: How AI will revolutionize education (and why that's a good thing). Penguin.
2.Zhai, X., & Lee, G. (2026). Artificial Intelligence for STEM Education Research. In Artificial Intelligence for STEM Education Research: Advanced Methods and Applications (pp. 1-15). Cham: Springer Nature Switzerland.
3.Zhai, X., & Krajcik, J. (Eds.). (2024). Uses of artificial intelligence in STEM education. Oxford University Press.
4.Sezginsoy Şeker, B., & İzgi Onbaşılı, Ü. (Ed.). (2025). Dijital çağın öğretmenleri: Yapay zekâ ile etkin ders tasarımı. Nobel Akademik Yayıncılık.
5.Atmaca, T., & Dağ, S. (Ed.). (2025). Eğitimde yapay zekâ ve uygulama alanları. Vizetek Yayıncılık.
6.Büyükköse, Ş. (Ed.). (2025). Matematik perspektifinden yapay zekâ. Vizetek Yayıncılık.
7.Karakaş, H., & Bircan, M. A. (Ed.). (2025). Etkinlik örnekleriyle eğitimde yapay zekâ uygulamaları. Vizetek Yayıncılık.
8.Nizam, F. (Ed.). (2025). Yapay zekâ çağı ve dijital iletişim: Fırsatlar, riskler, değişimler. Vizetek Yayıncılık.
Weekly Detailed Course Contents
Week 1 - Theoretical
Course Overview: Scope, Rationale, Importance, Rules, and Requirements
Week 2 - Theoretical
Introduction to Artificial Intelligence in Education: The basic concepts of artificial intelligence, its historical development, and its role in educational technology
Week 3 - Theoretical
The Role of Artificial Intelligence in Mathematics Education: The integration of artificial intelligence in STEM fields, opportunities, challenges, and pedagogical approaches.
Week 4 - Theoretical
Generative Artificial Intelligence and Language Models: The use of large language models (LLMs) such as ChatGPT and Claude in lesson planning and content creation.
Week 5 - Theoretical
Personalized Learning and Adaptive Systems: An examination of smart instructional systems tailored to students’ pace and level.
Week 6 - Theoretical
Data Analytics and Learning Analytics: Analyzing student performance data using artificial intelligence and identifying misconceptions.
Week 7 - Theoretical
Artificial Intelligence Tools in Mathematics Education: The use of intelligent software and platforms that support geometry, algebra, and problem-solving processes.
Week 8 - Theoretical
Ethics and Security in Artificial Intelligence in Education: Data privacy, copyright, bias in artificial intelligence, and principles of academic integrity.
Week 9 - Theoretical
AI-Supported Project Design (Planning): Problem identification, target audience analysis, and strategies for integrating AI tools into the project.
Week 10 - Theoretical
Interdisciplinary Project Development (Practicum - I): Preparing a draft and creating a prototype for a mathematics-focused, AI-supported project.
Week 11 - Theoretical
Interdisciplinary Project Development (Practicum - II): Preparing a draft and creating a prototype for a mathematics-focused, AI-supported project.
Week 12 - Theoretical
Artificial Intelligence in Assessment and Evaluation: Methods for AI-assisted test development, automated feedback, and rubric creation.
Week 13 - Theoretical
Testing and Improving Projects: Revising the developed projects based on peer reviews and expert feedback.
Week 14 - Theoretical
Project Presentations and Evaluation: Presentation of the AI-supported math projects developed and analysis of their instructional effectiveness.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures1%5
Assignment1%25
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory141356
Assignment55235
Reading140228
Individual Work140114
Final Examination116117
TOTAL WORKLOAD (hours)150
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
OÇ-1
4
4
4
4
3
2
2
4
4
OÇ-2
4
5
3
5
OÇ-3
3
4
4
3
4
3
OÇ-4
3
4
4
2
4
4
2
4
OÇ-5
3
5
4
5
3
Adnan Menderes University - Information Package / Course Catalogue
2026