Information Package / Course Catalogue
Artificial Intelligence Aided Design
Course Code: ÜKK251
Course Type: Required
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 1
Prt.: 1
Credit: 2
Lab: 0
ECTS: 2
Objectives of the Course

The objective of this course is to equip students with the proficiency to effectively use AI-based tools in engineering design processes. Students will learn how to generate technical drawings and 3D models from visual data, utilize Generative Design, automate documentation, and perform engineering analyses using AI tools.

Course Content

The course covers the world of AI-assisted design, starting from the basics and including the use of modern engineering tools. Over a 14-week period, topics ranging from visual-to-model conversion and automated technical documentation to material/manufacturing process selection and AI-driven design optimization are addressed through theory and practice.

Name of Lecturer(s)
Learning Outcomes
1.Effectively uses Artificial Intelligence tools and prompt engineering techniques in solving engineering problems.
2.Applies Artificial Intelligence software that converts visual data (photos/sketches) into technical drawings and 3D models.
3.Automates product structures (BOM), reporting, and technical documentation using Artificial Intelligence in the design process.
4.Performs material selection, manufacturing process determination, and engineering calculations with Artificial Intelligence-assisted tools.
5.Performs design optimization and simulation analyses using Generative Design principles.
Recommended or Required Reading
1.Kayhan, E. B. (2026). Artificial intelligence-supported design and technical drawing training videos [Unpublished educational video series]. Aydın Adnan Menderes University, Koçarlı Vocational School.
2.Kayhan, E. B. (2026). Artificial intelligence-supported design and technical drawing applications [Unpublished instructional materials]. Aydın Adnan Menderes University, Koçarlı Vocational School.
3.Onshape. (n.d.). Onshape [YouTube channel]. YouTube. Retrieved June 24, 2026, from https://www.youtube.com/@OnshapeInc
Weekly Detailed Course Contents
Week 1 - Theoretical & Practice
Introduction to AI: AI concepts, paradigm shift in engineering, and AI usage areas in design. Practice: Setting up the AI workspace and account configuration.
Week 2 - Theoretical & Practice
Prompt Engineering: Engineering-focused prompt creation, iterative processes, and techniques for effective dialogue with AI. Practice: Exercises in transcribing design ideas into prompts.
Week 3 - Theoretical & Practice
Visual to Technical Drawing: Vector technical drawing production from sketches and photos. Practice: Transitioning from sketches to technical views using tools like Vizcom.
Week 4 - Theoretical & Practice
Technical Drawing with AI: Automated dimensioning and standard-compliant image generation with AI tools. Practice: Converting AI-generated visuals into technical drawings.
Week 5 - Theoretical & Practice
Visual to 3D Model: Photogrammetry and AI-based 3D object creation techniques. Practice: Generating low-poly 3D models from photographs.
Week 6 - Theoretical & Practice
CAD Modeling Tools-I: AI plugins in parametric CAD software and automatic sketch creation. Practice: Modeling with in-software AI assistants.
Week 7 - Theoretical & Practice
CAD Modeling Tools-II: AI-driven form generation for complex geometries and surface modeling. Practice: AI-assisted form design exercises.
Week 8 - Theoretical & Practice
Documentation and Reporting: Automated writing of engineering reports and technical manuals with AI. Practice: Organizing technical reports using Large Language Models.
Week 9 - Theoretical & Practice
Bills of Materials (BOM): Generating parts lists with AI, ERP integration preparation, and data analytics. Practice: Creating and optimizing parts lists.
Week 10 - Theoretical & Practice
Material Selection: AI-assisted material database analysis based on engineering requirements. Practice: Creating a suitable material matrix with AI.
Week 11 - Theoretical & Practice
Manufacturing Method Determination: AI analysis of manufacturing suitability in terms of cost, time, and process. Practice: Design analysis and process selection.
Week 12 - Theoretical & Practice
Engineering Calculations: AI-based calculation modules, unit conversions, and formula validation. Practice: Validating strength and load calculations with AI.
Week 13 - Theoretical & Practice
Generative Design: Generative design algorithms, topology optimization, and lightweighting. Practice: Part weight reduction and form optimization with AI.
Week 14 - Theoretical & Practice
AI-Assisted Analysis/Simulation: Predicting simulation data with AI (Predictive Engineering) and result interpretation. Practice: Estimating stress/flow of designs using AI.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment1%10
Term Assignment1%10
Midterm Examination1%20
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140114
Lecture - Practice140114
Assignment1404
Term Project1628
Midterm Examination1213
Final Examination1617
TOTAL WORKLOAD (hours)50
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
PÇ-14
PÇ-15
OÇ-1
3
5
3
4
4
3
3
5
2
OÇ-2
4
2
3
5
3
4
2
4
OÇ-3
3
3
2
3
5
4
2
3
2
5
4
4
2
2
OÇ-4
5
2
5
3
4
4
5
3
3
4
4
3
OÇ-5
4
2
3
3
5
5
5
3
3
5
3
4
Adnan Menderes University - Information Package / Course Catalogue
2026