ARTIFICIAL INTELLIGENCE SUPPORTED COURSE AND ASSESSMENT DESIGN

EDS437

Course Code: EDS437
Semester: -
Theory+Practice: 2 + 0
ECTS: 4

Course Type

Elective Course

Course Level

Bachelor's Degree

Year of Study

Semester

ECTS

Name of Lecturer(s)

Learning Outcomes

At the end of this course, the students;

Explains the fundamental concepts related to artificial intelligence and educational technologies, and discusses their role in 21st-century learning environments.
Analyzes the potential benefits and limitations of artificial intelligence in education.
Effectively integrates artificial intelligence tools into lesson design processes.
Appropriately defines learning objectives in the process of developing AI-supported instructional content.
Designs instructional materials using generative artificial intelligence tools.
Identifies and explains assessment methods suitable for AI-supported lesson design.
Analyzes student data to ensure consistency in assessment processes.
Uses learning analytics to monitor student performance and develop decision-support systems.
Analyzes, designs, implements, and evaluates AI-supported instructional content and assessment tools.
Develops and implements personalized learning pathways using AI technologies.
Designs and applies adaptive assessment methods for diverse learner groups.

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

Course Description

Artificial intelligence (AI), educational technologies, and 21st-century learning environments; the impact of AI on education, its possibilities and limitations; principles of course design and the integration of AI tools; defining learning objectives using AI tools; creating instructional content with generative AI; assessment methods and types aligned with AI-supported course design; ensuring consistency in assessment design and analyzing student data; learning analytics and decision-support systems; analyzing, designing, implementing, and evaluating AI-supported instructional content and assessment tools; developing personalized learning pathways with AI; and creating adaptive assessment methods for diverse learner groups.

Course Contents

WEEK	TOPICS
1^st Week	AI, Educational Technologies, and 21st-Century Learning Environments
2^nd Week	The Impact of AI on Education: Possibilities and Limitations
3^rd Week	Course Design Principles and Integration of AI Tools
4^th Week	Determining Learning Objectives Using AI Tools
5^th Week	Creating Instructional Content with Generative AI
6^th Week	Assessment Methods and Types Based on AI-Enhanced Course Design
7^th Week	Ensuring Consistency in Assessment Design and Analyzing Student Data
8^th Week	Learning Analytics and Decision Support Systems
9^th Week	Analysis, Design, Implementation, and Evaluation of AI-Enhanced Content and Assessment Tools
10^th Week	Personalized Learning Paths with AI
11^th Week	Developing Adaptive Assessment Methods for Different Learner Groups
12^th Week	Applied Study: Learning Analytics-Based Intervention Design
13^th Week	Project Presentations I: Course and Assessment Design
14^th Week	Project Presentations II & Overall Assessment and Feedback
15^th Week	Overview

Recommended or Required Reading

Luckin, R. (2018). Machine Learning and Human Intelligence: The Future of Education for the 21st Century. UCL IOE Press.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign
Stanford CAIAF Framework. (2024, Haziran 1). Enhancing Educational Evaluation With Ethical AI Integration. Stanford University.
Cambridge International. (2023, Kasım). The use of generative AI in coursework. Cambridge International

Planned Learning Activities and Teaching Methods

Lecture
Discussion
Questions/Answers
Presentation

Assessment Methods and Criteria

	Quantity	Percentage (%)
Mid-term	1	30
Assignment	1	10
Practice	4	20
Total(%)		60
Contribution of In-term Studies to Overall Grade(%)		60
Contribution of Final Examination to Overall Grade(%)		40
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	20	20
Course hours	13	2	26
Preparation for Midterm exam	1	14	14
Laboratory (including preparation)	14	1	14
Final exam	1	2	2
Homework
Total Work Load			120
Total Work Load / 30 (h)			4
ECTS Credit of the Course			4

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4	LO5	LO6	LO7	LO8	LO9	LO10	LO11
K1				X	X						X
K2				X							X
K3					X	X	X	X
K4					X	X
K5	X
K6
K7	X	X
K8	X	X	X	X	X	X	X	X	X	X	X
K9
K10