Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
HUK 1071	ARTIFICIAL INTELLIGENCE AND PERSONAL DATA	ELECTIVE	2	0	0	4

Offered By

Law

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR EDA ODAMAN

Offered to

Law

Course Objective

The purpose of the course is to explain the concept of artificial intelligence and to evaluate the legal remedies for the protection of personal data in the age of artificial intelligence.

Learning Outcomes of the Course Unit

1	To be able to define artificial intelligence and its implications in our law
2	To have a good command of the legislation on the protection of personal data in general in our law
3	Explain the concept of data processing in the age of artificial intelligence
4	To be able to identify the problems encountered in the protection of personal data in the age of artificial intelligence
5	To be able to produce solutions to the problems encountered in the protection of personal data in the age of artificial intelligence and the ways that can be followed for the solution

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	1-Definition of artificial intelligence
2	2-Types of artificial intelligence
3	3-Artificial intelligence applications
4	4-The concept of personal data
5	5-Types of personal data
6	6-Methods of protecting personal data
7	7-International regulations on the protection of personal data
8	8-Regulations on the protection of personal data in Turkish Law
9	9-The relationship between artificial intelligence and personal data protection
10	10-Ethical dimensions of personal data processing in the age of artificial intelligence
11	11-Challenges to the protection of personal data in the age of artificial intelligence
12	12-Instruments to protect personal data in the age of artificial intelligence
13	13-MIDTERM
14	14-The convenience of the artificial intelligence era for the protection of personal data
15	15-Evaluation of doctrinal opinions on artificial intelligence and personal data protection

Recomended or Required Reading

AŞIKOĞLU, Şehriban Ipek. Avrupa Birliği ve Türk Hukukunda Kişisel Verilerin Korunması ve Büyük Veri. Istanbul: On Iki Levha Yayıncılık. 2018.

ÇEKIN,Mesut Serdar/ BERKTAŞ,Ahmet Esad/ AKINCI, Muhammed Furkan. Veri Hukuku. Istanbul: On Iki Levha Yayıncılık. 2023.

EKMEKÇI, Ömer/ YÜCEDAĞ, Nafiye/ AKKANAT ÖZTÜRK, Elif Beyza/ AŞIKOĞLU, Şehriban Ipek. Kişisel Verilerin Korunması Hukuku, Istanbul: On Iki Levha Yayıncılık. 2024.

ERSOY, Çağlar. Robotlar. Yapay Zekâ ve Hukuk. 3.b. Istanbul: Onikilevha Yayıncılık. 2019.

ERGÜL, Ergin. Avrupa Yapay Zeka Hukuku. Ankara: Adalet Yayınevi. 2025.

GÜÇLÜTÜRK, Osman Gazi. Yapay Zeka ve Verinin Kullanımı. Istanbul: On Iki Levha Yayıncılık. 2022.

KAYA, Mehmet Bedii. KVKK Reformu: 2024 Değişiklikleri. Dijital Baskı 1.0. 2024. https://mbkaya.com/hukuk/kvkk-reformu.pdf.

TURAN, Metin.Karşılaştırmalı Hukukta Kişisel Verilerin Korunması. 5. b. Ankara:Seçkin Yayıncılık. 2024.

Planned Learning Activities and Teaching Methods

In the course of artificial intelligence and personal data; In addition to lectures in the light of the opinions in the doctrine, examples from practice, especially the decisions of the Court of Cassation and Federal Court decisions, lectures are also made in the form of case solutions.

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	MTE	MIDTERM EXAM
2	MTE	MIDTERM EXAM
3	FCG	FINAL COURSE GRADE
4	FCG	FINAL COURSE GRADE	roundKPU + FN,0
5	RST	RESIT
6	FCGR	FINAL COURSE GRADE (RESIT)	roundKPU + BUT,0

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

eda.odaman@deu.edu.tr

Office Hours

Monday, 13:00-15:00

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Lectures	14	2	28
Preparations before/after weekly lectures	14	2	28
Preparation for final exam	1	25	25
Preparation for midterm exam	1	24	24
Final	1	2	2
Midterm	1	1	1
TOTAL WORKLOAD (hours)			108

Contribution of Learning Outcomes to Programme Outcomes

PO/LO	PO.1	PO.2	PO.3	PO.4	PO.5	PO.6	PO.7	PO.8	PO.9	PO.10	PO.11	PO.12	PO.13	PO.14	PO.15
LO.1	1	1	1	1	1	1	1	1		1	1	1	1	1	1
LO.2	1	1	1	1	1	1	1	1	1		1	1	1	1	1
LO.3	1	1	1	1	1	1		1		1		1	1	1	1
LO.4	1	1	1	1	1	1	1	1	1	1		1	1	1	1
LO.5	1	1	1	1	1	1		1	1		1	1	1	1	1

COURSE UNIT TITLE

DEGREE PROGRAMMES

Description of Individual Course Units

Offered By

Level of Course Unit

Course Coordinator

Offered to

Course Objective

Learning Outcomes of the Course Unit

Mode of Delivery

Prerequisites and Co-requisites

Recomended Optional Programme Components

Course Contents

Recomended or Required Reading

Planned Learning Activities and Teaching Methods

Assessment Methods

Further Notes About Assessment Methods

Assessment Criteria

Language of Instruction

Course Policies and Rules

Contact Details for the Lecturer(s)

Office Hours

Work Placement(s)

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes

CONTACT INFORMATION