| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 4 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Evaluates the questions produced by artificial intelligence from a psychometric point of view.
- Evaluates open-ended question responses generated by AI and real raters and compares their results.
- Interpret the results obtained by combining artificial intelligence and classical measurement methods.
- Examines the limitations of artificial intelligence in measurement and evaluation processes.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | |
|---|
| Course Description | This course focuses on the effects of Artificial Intelligence on Assessment and Evaluation in Education. In the course, the transformative effect of Artificial Intelligence in assessment and evaluation, the use of Artificial Intelligence in assessment and evaluation, applications and the role of teachers in this context are examined. Ethical principles in the use of AI and responsible use of AI are discussed. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Artificial Intelligence and Assessment in Education: Introduction and Basic Concepts |
|
2nd Week |
The role of assessment and evaluation in teaching, concepts changing with AI, and teacher competencies |
|
3rd Week |
Advantages and risks of using AI in Assessment and Evaluation |
|
4th Week |
Comparison of Traditional Assessment and Evaluation with AI-supported Assessment and Evaluation |
|
5th Week |
Automatic Item Generation: The role of large language models |
|
6th Week |
Application Strategies for Automatic Item Generation |
|
7th Week |
Automatic Scoring and Feedback Systems |
|
8th Week |
Midterm exam |
|
9th Week |
Application Strategies for Automatic Scoring and Feedback |
|
10th Week |
The Complementary Role of Teachers in AI-Supported Measurement and Evaluation |
|
11th Week |
Ethics, Data Privacy |
|
12th Week |
Bias, Hallucination |
|
13th Week |
Artificial Intelligence Tools Used in Measurement and Evaluation |
|
14th Week |
Artificial Intelligence Tools Used in Measurement and Evaluation |
|
15th Week |
Overview |
|
| Recommended or Required Reading | |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Discussion
- Questions/Answers
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
20 |
| Assignment |
3 |
30 |
| Total(%) |
|
50 |
| Contribution of In-term Studies to Overall Grade(%) |
|
50 |
| Contribution of Final Examination to Overall Grade(%) |
|
50 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
3 |
42 |
| Preparation for Final exam |
1 |
12 |
12 |
| Course hours |
13 |
2 |
26 |
| Preparation for Midterm exam |
1 |
12 |
12 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
3 |
8 |
24 |
| Total Work Load | 120 |
| Total Work Load / 30 (h) | 4 |
| ECTS Credit of the Course | 4 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
