COURSE UNIT TITLE

: FIELD ELC. 8 (ARTıFıCıAL INTELLıGENCE APPLıCATıONS ıN MATHEMATıCS EDUCATıON)

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IMÖ 4010 FIELD ELC. 8 (ARTıFıCıAL INTELLıGENCE APPLıCATıONS ıN MATHEMATıCS EDUCATıON) ELECTIVE 2 0 0 4

Offered By

ELEMENTARY MATHEMATICS TEACHER EDUCATION

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR YUSUF ERKUŞ

Offered to

ELEMENTARY MATHEMATICS TEACHER EDUCATION

Course Objective

The purpose of this course is to introduce primary school mathematics teacher candidates to the basic concepts of artificial intelligence and their potential applications in mathematics education, to provide them with the ability to evaluate and use artificial intelligence-supported tools in line with pedagogical principles, to raise awareness about the ethical and social dimensions of these technologies, and to provide a vision of how they can integrate artificial intelligence in their future professional development.

Learning Outcomes of the Course Unit

1   Explain the basic concepts of Artificial Intelligence (AI) (e.g., machine learning, natural language processing) and its evolutionary place within educational technologies.
2   Discuss the transformative potential and opportunity areas of AI in the education sector in general.
3   Provide examples of specific AI-supported tools and platforms developed or adapted for primary mathematics education and describe their basic functions.
4   Critically analyze the pedagogical suitability, instructional benefits, and usage difficulties of AI-supported mathematics education tools.
5   Develop strategies for integrating AI-powered tools into primary school mathematics curricula appropriate to course objectives and student needs, and draft sample lesson scenarios.
6   Assess critical issues such as ethics (bias, transparency), privacy (data security), and accessibility associated with the use of AI in education and demonstrate awareness of potential risks.
7   Can use a simple and widely accessible AI-assisted mathematics learning tool or platform at a basic level and gain experience in its pedagogical use.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to the Course, Introduction, Expectations and Course Structure
2 Introduction to Artificial Intelligence: Fundamentals, Development, Working Principle and Limitations
3 Overview of Artificial Intelligence Applications in Education: Opportunities and Areas
4 Applications of Artificial Intelligence in Mathematics Education: Sample Tools and Pedagogical Analysis
5 Deep Dive 1: Adaptive Learning Systems and Mathematics Teaching
6 Deep Dive 2: Mathematical Assessment and Feedback with AI
7 Deep Dive 3: AI and Mathematical Content Generation (Using LLM)
8 General review, course evaluation, midterm exam
9 Ethical, Privacy and Accessibility Dimensions of the Use of Artificial Intelligence in Education
10 The Teacher Role in the Age of Artificial Intelligence: Changing Responsibilities and Skills
11 AI-Supported Learning Environments Design and Integration Strategies
12 Student Project Presentations I: AI-Powered Mathematics Education App Ideas
13 Student Project Presentations II: AI-Powered Mathematics Education App Ideas
14 Student Project Presentations III: AI-Powered Mathematics Education App Ideas
15 Final Exam

Recomended or Required Reading

Görgüt, R. Ç. (2024). Yapay Zeka ve Matematik Eğitimi. Eğitim & Bilim 2023-IV, 43.

Planned Learning Activities and Teaching Methods

Lectures, discussions, questions and answers, observations, group work, case studies.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 VZ Midterm
2 FN Semester final exam
3 BNS BNS Student examVZ * 0.40 + Student examFN * 0.60
4 BUT Make-up note
5 BBN End of make-up grade Student examVZ * 0.40 + Student examBUT * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

Assessment of students is measured by midterm and final exams in line with the learning outcomes.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

yusuf.erkus@deu.edu.tr

Office Hours

It will be announced at the beginning of the semester.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 2 26
Preparations before/after weekly lectures 13 2 26
Preparation for midterm exam 1 15 15
Preparation for final exam 1 20 20
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 91

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15
LO.112122311111322
LO.233214411332423
LO.314233333334234
LO.445434445455445
LO.535334345445345
LO.654424422343433
LO.714353334334344

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

HOME / ABOUT US / DEGREE PROGRAMMES / GENERAL INFORMATION FOR STUDENTS / INTERNATIONAL RELATIONS