COURSE UNIT TITLE

: ARTIFICIAL INTELLIGENCE APPLICATIONS IN LANGUAGE AND LITERATURE RESEARCH

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STD 5066 ARTIFICIAL INTELLIGENCE APPLICATIONS IN LANGUAGE AND LITERATURE RESEARCH ELECTIVE 3 0 0 6

Offered By

Turkish Language and Literature Teacher Education

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSISTANT PROFESSOR MUSTAFA ÖZBAŞ

Offered to

Turkish Language and Literature Teacher Education

Course Objective

This course aims to introduce the historical development, fundamental concepts, and current applications of artificial intelligence technologies in language and literature studies, and to enable students to use these technologies consciously and creatively in their academic work.

Learning Outcomes of the Course Unit

1   Explains the history and key concepts of artificial intelligence and related technologies.
2   Identifies and classifies AI tools used in language and literature studies.
3   Applies AI-supported methods in literary text analysis.
4   Evaluates the potential of AI-based tools in language teaching.
5   Discusses the cultural and academic impact of AI applications from an ethical and critical perspective.

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: Overview of AI, basic concepts
2 History of AI and its relationship with literature
3 Use of technology in education and digital literacy
4 AI applications in linguistics: Lexical analysis, semantics
5 Natural Language Processing (NLP) tools in literary analysis
6 Literary production using large language models (e.g. ChatGPT, Claude, Copilot)
7 Poetry, story, and essay generation with AI and ethical debates
8 Midterm Exam
9 AI in language teaching: Smart content creators, writing assistants
10 Literary genre recognition and text classification with AI
11 Data mining and thematic analysis in literature
12 AI-supported storytelling with visual-audio materials
13 AI and creative writing: Student workshops
14 AI and cultural representations: Ideology, gender, diversity
15 General evaluation, student project presentations
16 Final Exam

Recomended or Required Reading

Akyel, Y., & Tur, E. (2024). Yapay zekanın potansiyelinin ve eğitim bilimlerindeki uygulamalarının araştırılması ve araştırmalarda beklentiler, zorluklar ve gelecek yönelimleri. Ahi Evran Üniversitesi Kırşehir Eğitim Fakültesi Dergisi, 25(1), 645-711.
Arf, C. (1959). Makine düşünebilir mi ve nasıl düşünebilir Atatürk Üniversitesi 1958-1959 Öğretim Yılı Halk Konferansları I, Atatürk Üniversitesi Üniversite Çalışmalarını Muhite Yayma ve Halk Eğitimi Yayınları Konferanslar Serisi, No. I.
Arslan, K. (2020). Eğitimde yapay zeka ve uygulamaları. Batı Anadolu Eğitim Bilimleri Dergisi, 11(1), 71-88.
Görü Doğan, T. (2024). Yapay Zekâ Okuryazarlığı ve Yeni Medya Eğitmi Üzerine Kavramsal Bir Değerlendirme. Yeni Medya Çalışmaları ve Yapay Zeka I içinde (Eds. Deniz Yengin, Tamer Bayrak), 473-520, IKSAD Publishing House, Ankara.
Incemen, S., Öztürk, G. (2024). Farklı eğitim alanlarında yapay zekâ: Uygulama örnekleri. International Journal of Computers in Education, 7(1), 27-49.
Russell, S., Norvig, P. (2021). Artificial Intelligence: A Modern Approach, Global Edition.(4th Ed.), Pearson.
Seyrek, M., Yıldız, S., Emeksiz, H., Şahin, A., & Türkmen, M. T. (2024). Öğretmenlerin eğitimde yapay zekâ kullanımına yönelik algıları. International Journal of Social and Humanities Sciences Research (JSHSR), 11(106), 845-856.
Uçar, S. (2023). Eğitimde yapay zekâ dönemi: ChatGPT kullanımın faydaları ve olası zorlukları. Eğitim ve Bilim, 7.
UNESCO (2023). ChatGPT and Artificial Intelligence in Higher Education. Quick Start Guide. UNESCO, Paris and Caracas.
Yu, S., Lu, Y. (2021). An Introduction to Artificial Intelligence in Education. Springer Singapore.

Planned Learning Activities and Teaching Methods

Lecture, presentation, discussion, question-answer

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FINS FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + ASG * 0.10 + FINS * 0.60
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.10 + RST * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

Students taking the course are required to follow the lessons regularly and fulfill the obligations of the course in a complete and timely manner.

Contact Details for the Lecturer(s)

Assist. Prof. Dr. Mustafa Özbaş
Social Sciences Building, Z-06
Tel.:0 232 3012106
mustafa.ozbas@deu.edu.tr

Office Hours

Wednesday, 16.00-17.00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 13 3 39
Preparation for midterm exam 1 10 10
Preparation for final exam 1 15 15
Preparing assignments 1 40 40
Midterm 1 3 3
Final 1 3 3
TOTAL WORKLOAD (hours) 152

Contribution of Learning Outcomes to Programme Outcomes

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LO.2555555555555
LO.3555555555555
LO.4555555555555
LO.5555555555555

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

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