AI AND MACHINE TRANSLATION PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
ETI216	AI AND MACHINE TRANSLATION	4	3	3	6

GENERAL INFORMATION
Language of Instruction :	English
Level of the Course Unit :	BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle
Type of the Course :	Compulsory
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit	Assist.Prof. VAHİDE METİN
Instructor(s) of the Course Unit
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	The aim of this course is to enable students to understand the basic working principles, opportunities, and limitations of artificial intelligence and machine translation technologies, and to develop their skills in evaluating translation outputs, post-editing, and effective prompt design. The course also aims to raise awareness of the ethical, legal, and professional dimensions of AI use and the changing role of the translator.
Contents of the Course Unit:	This course examines the historical, theoretical, and technological development of machine translation through a chronological approach, from the earliest ideas of mechanical translation to contemporary artificial intelligence and large language model-based systems. The course covers the emergence of machine translation, early attempts at automatic translation, rule-based machine translation, direct translation, transfer-based translation, the interlingua approach, example-based machine translation, corpus-based methods, statistical machine translation, hybrid systems, neural machine translation, attention mechanisms, Transformer architecture, multilingual systems, large language models, and generative AI applications. Students learn the fundamental working principles of each machine translation approach, the linguistic or numerical data they rely on, their strengths and weaknesses, common error types, and their impact on translators’ workflows. Outputs produced by systems developed in different periods are compared using the same or similar texts in terms of accuracy, fluency, terminology, context, consistency, cultural appropriateness, and suitability for text type. The course also covers the integration of machine translation with computer-assisted translation tools, the use of translation memories and terminology databases, source-text pre-editing, the post-editing of machine translation output, and the evaluation of translation quality through both human and automatic methods. The final part of the course focuses on the use of large language models and generative AI systems in translation. Students carry out practical activities involving translation-oriented prompt design, providing contextual and terminological information, generating alternative translations, verifying AI-generated outputs, and designing translation workflows based on human–AI collaboration. In addition, the course addresses ethical and legal issues related to the use of artificial intelligence and machine translation, including confidentiality, data security, protection of personal data, copyright, data ownership, algorithmic bias, hallucinated information, transparency, academic integrity, and the professional responsibility of translators.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Explains the historical, theoretical, and technological development of machine translation.
Compares the fundamental principles of rule-based, example-based, statistical, and neural machine translation approaches and large language model-based translation systems.
Evaluates the outputs of different machine translation and AI systems in terms of accuracy, fluency, terminology, consistency, context, and cultural appropriateness.
Identifies error types in machine translation output and applies appropriate post-editing and quality control methods.
Explains and evaluates the integration of machine translation with translation memories, terminology databases, and computer-assisted translation tools.
Designs translation-oriented prompts for large language models and generative AI systems and guides and verifies outputs by providing contextual, terminological, and stylistic information.
Evaluates ethical and legal issues related to AI and machine translation, including confidentiality, data security, copyright, algorithmic bias, transparency, and professional responsibility.
Evaluates the changing role of the translator in AI-assisted translation environments and emerging workflows based on human–AI collaboration.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	-	Introduction to the class	-
2	-	The emergence of the idea of automatic translation; mechanical dictionaries and language machines; early ideas about the use of computers in language translation; the development of machine translation research after the Second World War; early automatic translation experiments; the Georgetown–IBM experiment; expectations and limitations in the early period of machine translation.	In-class discussion
3	-	The basic operating principles of rule-based machine translation; grammatical rules, dictionaries, morphological analysis, and syntactic analysis; the stages of source-language analysis, transfer, and target-text generation; encoding linguistic knowledge into systems.	In-class discussion
4	-	The direct translation approach; direct transfer of words and structures from the source language to the target language; transfer-based systems; lexical and structural transfer rules; the interlingua approach; comparison of language-pair-dependent and language-independent systems; the Vauquois Triangle.	In-class discussion
5	-	Dictionary development in rule-based systems; morphological and syntactic ambiguities; idioms, polysemous words, and cultural expressions; the cost of manually creating language rules; problems encountered across different language pairs; analysis of rule-based system outputs.	In-class discussion
6	-	The emergence of example-based machine translation; translation based on similarity and analogy; reuse of previously translated sentences and expressions; matching, segmenting, and recombining examples; the relationship between translation memory and example-based machine translation.	In-class discussion
7	-	The concepts of monolingual, bilingual, and parallel corpora; alignment of source and target texts; sentence- and word-level alignment; the impact of corpus size and quality on system performance; training, validation, and test data; data cleaning and data preparation.	In-class discussion
8	-	The emergence of statistical machine translation; probability-based approaches to translation; translation models and language models; word-based statistical machine translation; word alignment; determining the most probable target sentence for a given source sentence.	In-class discussion
9	-	Example-based statistical machine translation; translation of groups of words rather than individual words; reordering models; n-gram language models; hierarchical and syntax-based statistical systems; fluency and accuracy problems in statistical systems.	In-class discussion
10	-	MID-TERM EXAM	-
11	-	Combining rule-based and statistical methods; hybrid machine translation systems; making use of the strengths of different systems; integration of machine translation into computer-assisted translation tools; combined use of translation memories, terminology databases, and machine translation suggestions.	In-class discussion
12	-	Sequence-to-sequence models; problems encountered in translating long sentences; the emergence of the attention mechanism; focusing on different parts of the source sentence; use of contextual information; fluency, accuracy, omission, and addition errors in neural machine translation.	In-class discussion
13	-	The emergence of Transformer architecture; the self-attention mechanism; encoder and decoder layers; parallel processing and the use of large datasets; multilingual neural machine translation; zero-shot translation; domain adaptation; low-resource languages; customization of neural machine translation systems.	In-class discussion
14	-	Basic working principles of large language models; pretrained language models; direct translation with generative AI; providing context, purpose, target audience, terminology, and style information; translation-oriented prompt design; step-by-step prompting; comparison of machine translation engines and large language models; risks of hallucinated information and meaning shifts in AI-generated outputs.	In-class discussion
15	-	Comparison of human translation, machine translation, and AI-assisted translation; light and full post-editing of machine translation and AI-generated translation; error classification and quality assessment; the translator’s ultimate responsibility; confidentiality, data security, copyright, algorithmic bias, and transparency; disclosure of AI use; the changing professional roles of translators and future human–AI workflows.	In-class discussion
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description	Examination Method

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Knows translation theories, linguistic approaches, and intercultural communication models.				3
2	Explains fundamental concepts related to the relationship between language, culture, and meaning.				3
3	Evaluates the historical development and theoretical orientations of the field of translation studies.	0

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Identifies the grammatical, semantic, and cultural characteristics of source and target languages.				3
2	Has knowledge of the structural and functional features of various text types.					4
3	Recognizes institutions, professional standards, and ethical principles related to the translation industry.					4

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Analyzes written and oral texts in terms of language, meaning, and context.	0
2	Determines appropriate translation strategies and reconstructs meaning with consideration of cultural differences.	0
3	Evaluates translation problems through critical thinking and problem-solving skills.					4

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Effectively uses professional tools and technological software in written and oral translation processes.						5
2	Performs translation practices in various fields such as law, literature, technology, and media.					4
3	Manages translation projects by observing time management and quality standards.	0

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Plans, conducts, and evaluates translation projects individually or as part of a team.	0
2	Solves problems in translation processes by making independent decisions.	0
3	Takes responsibility in professional practices and demonstrates leadership when necessary.	0
4	Works collaboratively with different stakeholders to achieve common goals.	0

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Follows new research and technological developments in the field.						5
2	Continuously improves professional knowledge and skills through lifelong learning.					4
3	Identifies personal learning needs and applies independent learning strategies.	0

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Communicates effectively in multicultural environments and contributes to teamwork.	0
2	Uses English and Turkish fluently and accurately in professional and social contexts.	0
3	Respects intercultural differences and acts in accordance with ethical values.	0

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Adheres to the principles of confidentiality, impartiality, and accuracy in the translation process.	0
2	Assumes professional responsibility and makes ethical decisions in translation projects.	0
3	Acts professionally in accordance with national and international standards of the field.						5

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
Lecture & In-Class Activities	14	3	42
Preliminary & Further Study	6	6	36
Land Surveying	0	0	0
Group Work	7	7	49
Laboratory	0	0	0
Reading	4	4	16
Assignment (Homework)	2	2	4
Project Work	2	2	4
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	0	0	0
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	0	0	0
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	0	0	0
Final Exam	1	1	1
Preparation for the Final Exam	0	0	0
Mid-Term Exam	1	1	1
Preparation for the Mid-Term Exam	0	0	0
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	37	0	153