ETI 381 | Course Introduction and Application Information - Department of English Translation and Interpreting

Course Name

AI and Translation

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
ETI 381	Fall/Spring	3	0	3	6

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	First Cycle
Mode of Delivery	face to face
Teaching Methods and Techniques of the Course	Discussion Group Work Q&A Critical feedback Lecture / Presentation
National Occupation Classification	-
Course Coordinator	-
Course Lecturer(s)	Doç. Dr. Ilgın AKTENER
Assistant(s)	Araş. Gör. Aslı Melike SOYLU Araş. Gör. Yaşar Akgün

Course Objectives	This course aims to enhance translation processes and to equip students with the knowledge and skills to evaluate and utilize AI tools to adapt to evolving industry demands.
Learning Outcomes	The students who succeeded in this course; compare the key features, strengths, and limitations of different AI tools. integrate AI tools into translation processes. carry out post-editing. discuss the history of AI, its use in different professions, and ethical issues related to AI. evaluate the efficiency of AI tools in translation across different text types.
Course Description	This course is designed for the integration of artificial intelligence into translation studies applications. Students will use AI tools in translation and post-editing processes in a laboratory equipped with relevant technologies
Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses
	Supportive Courses	X
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation
1	Introduction to Artificial Intelligence: The History and Future of Artificial Intelligence	Michael Haenlein and Andreas Kaplan, A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence (2019).
2	Introduction to Artificial Intelligence: Fundamental Skills and Ethical Issues	Andreas Kaplan and Michael Haenlein, "Siri, Siri, in My Hand: Who’s the Fairest in the Land? On the Interpretations, Illustrations, and Implications of Artificial Intelligence" (2019). Ming-Hui Huang, Roland Rust, and Vjislav Maksimovic, The Feeling Economy: Managing in the Next Generation of Artificial Intelligence (AI) (2019).
3	Introduction to Artificial Intelligence and Translation: Fundamental Skills and Tools, Prompts, Human Agency	Miguel Jimenez-Crespo, "The 'Technological Turn' in Translation Studies: Are We There Yet? A Transversal Cross-Disciplinary Approach," in Translation Spaces, vol. 9 (2020), doi:10.1075/ts.19012.jim. Mikel L. Forcada, (2017). Making sense of neural machine translation. Translation Spaces, 6(2), 291–309. doi:10.1075/ts.6.2.06for David Katan, "Tools for Transforming Translators into Homo Narrans or 'What Machines Can't Do'," in What Machines Can't Do (2022), doi:10.4324/9781003223344-6. Feyza Dalaylı, "Use of NLP Techniques in Translation by ChatGPT: Case Study" (2023).
4	Artificial Intelligence and Translation Practices	Nilgin T. Polat, "Yapay Zekâ ve Çeviri: Mütercim-Tercümanlık Alanında Yeni Bir Paradigma," in Diyalog: Interkulturelle Zeitschrift für Germanistik, vol. 11, no. 2 (2023), 482-487.
5	Artificial Intelligence and Translation Practices	Various Texts
6	Artificial Intelligence and Translation Practices	Various Texts
7	Artificial Intelligence and Translation Practices	Various Texts
8	Artificial Intelligence and Translation Practices with Different Text Types	Various Texts
9	Midterm Exam
10	AI, Post-Editing, and Writing Skills	Lucas N. Vieira, "Post-Editing of Machine Translation," chap. in The Routledge Handbook of Translation and Technology, ed. M. O’Hagan (London and New York: Routledge, 2019), 319-335. Caner Çetiner, "Makine Çevirisi Sonrası Düzeltme İşlemine (Post-Editing) Yönelik Kapsamlı Bir İnceleme," in RumeliDE Dil ve Edebiyat Araştırmaları Dergisi, no. Ö6 (2019), 462-472, doi:10.29000/rumelide.649333.
11	AI, Post-Editing, and Writing Skills	Özlem Temizöz, "Postediting Machine Translation Output: Subject-Matter Experts versus Professional Translators" (2014).
12	Student Presentations
13	Student Presentations
14	Student Presentations
15	Review of the Term
16	Final Exam

Course Notes/Textbooks

Michael Haenlein and Andreas Kaplan, A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence (2019).

Andreas Kaplan and Michael Haenlein, "Siri, Siri, in My Hand: Who’s the Fairest in the Land? On the Interpretations, Illustrations, and Implications of Artificial Intelligence" (2019).

Ming-Hui Huang, Roland Rust, and Vjislav Maksimovic, The Feeling Economy: Managing in the Next Generation of Artificial Intelligence (AI) (2019).

Miguel Jimenez-Crespo, "The 'Technological Turn' in Translation Studies: Are We There Yet? A Transversal Cross-Disciplinary Approach," in Translation Spaces, vol. 9 (2020), doi:10.1075/ts.19012.jim.

Mikel L. Forcada, (2017). Making sense of neural machine translation. Translation Spaces, 6(2), 291–309. doi:10.1075/ts.6.2.06for

David Katan, "Tools for Transforming Translators into Homo Narrans or 'What Machines Can't Do'," in What Machines Can't Do (2022), doi:10.4324/9781003223344-6.

Feyza Dalaylı, "Use of NLP Techniques in Translation by ChatGPT: Case Study" (2023).

Nilgün T. Polat, "Yapay Zekâ ve Çeviri: Mütercim-Tercümanlık Alanında Yeni Bir Paradigma," in Diyalog: Interkulturelle Zeitschrift für Germanistik, vol. 11, no. 2 (2023), 482-487.

Lucas N. Vieira, "Post-Editing of Machine Translation," chap. in The Routledge Handbook of Translation and Technology, ed. M. O’Hagan (London and New York: Routledge, 2019), 319-335.

Caner Çetiner, "Makine Çevirisi Sonrası Düzeltme İşlemine (Post-Editing) Yönelik Kapsamlı Bir İnceleme," in RumeliDE Dil ve Edebiyat Araştırmaları Dergisi, no. Ö6 (2019), 462-472, doi:10.29000/rumelide.649333.

Özlem Temizöz, "Postediting Machine Translation Output: Subject-Matter Experts versus Professional Translators" (2014

Suggested Readings/Materials

Dorothy Kenny, ed., Machine Translation for Everyone: Empowering Users in the Age of Artificial Intelligence, Translation and Multilingual Natural Language Processing, vol. 18 (Berlin: Language Science Press, 2022), doi:10.5281/zenodo.6653406. ISBN-13 (15) 978-3-96110-348-5

Semester Activities	Number	Weigthing
Participation	1	10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury	1	20
Project	1	40
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam
Total

Weighting of Semester Activities on the Final Grade	4	100
Weighting of End-of-Semester Activities on the Final Grade
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	14	3	42
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury	1	20	20
Project	1	40	40
Seminar / Workshop			0
Oral Exam			0
Midterms	1	30	30
Final Exam			0
		Total	180

#	Program Competencies/Outcomes	* Contribution Level
#	Program Competencies/Outcomes	1	2	3	4	5
1	To be able to use advanced, field-specific conceptual, theoretical, and practical knowledge acquired,	-	X	-	-	-
2	To be able to analyze and research field-specific concepts and ideas and to interpret data individually or as a team using scientific methods,	-	X	-	-	-
3	To be able to understand and use grammatical and semantic structures of the source and target languages,	-	-	-	-	-
4	To be able to obtain information about social, cultural, and historical approaches within the source and target languages and to use this information for textual analysis and production,	-	-	-	-	-
5	To be able to understand and interpret written and oral texts in the source language and to transfer these texts into the target language using a semantically and functionally appropriate language,	-	-	-	-	-
6	To be able to produce creative translations and assess the translation products critically by defining the steps, strategies and problems in the translation process in the light of field-specific theoretical knowledge and skills acquired,	-	-	-	-	X
7	To be able to transfer the theoretical knowledge and research skills within different areas of expertise to translational act,	-	-	-	-	-
8	To be able to use computer-assisted translation tools and machine translation effectively at each step of the translation process, and to follow the theoretical and practical developments in these fields,	-	-	-	-	X
9	To be able to gain awareness of the translator’s social role, job profile, and professional ethical values and to acquire workload management skills for individual or team work,	X	-	-	-	-
10	To be able to access necessary sources to improve quality at each step of the translation process and to assess the target text in accordance with the quality objectives by using these sources,	-	-	-	-	-
11	To be able to establish effective oral and written communication skills both in English and Turkish, to be able to speak a second foreign language at a good level, to be able to use a third foreign language at intermediate level,	-	-	-	-	-
12	To be able to relate the knowledge accumulated throughout the human history to their field of expertise.	-	-	-	-	-