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Language and Artificial Intelligence Management
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Summary of the Profile
General Description:
Objective(s) of a study programme:
To train analytical, critically thinking and creative (English) language professionals and generative Artificial Intelligence (AI) Prompt Engineers who can successfully apply language and AI knowledge in a wide range of domains (research, security, policy, media, etc.) and create meaningful and effective human-machine communication using specific prompts to help AI to understand and perform the desired tasks.
Learning outcomes:
1. Knowledge and its application
1.1. The student will understand and describe language as a constantly changing phenomenon.
1.2. The student will know and will be able to correctly use and explain basic linguistics concepts and terms; will know and be able to describe the main fields of linguistics.
1.3. The student will be able to understand, describe, analyse and evaluate natural and AI-generated language phenomena (and/or texts) at different levels (phonetic, phonological, morphological, syntactic, semantic, pragmatic, etc.), using appropriate terminology, drawing on different linguistic theories, and employing appropriate methods.
1.4. The student will have knowledge of basic programming languages and tools, and the ability to understand and explain the process of developing AI systems (from data collection and processing to model training and evaluation), generative AI technologies, models and algorithms, and to put them into practice in the development or improvement of IoT systems.
1.5. The student will master advanced machine learning technologies and algorithms, and acquire the skills to ensure effective interaction between computer and human language using natural language processing (NLP) techniques.
1.6. The student will know how to create, evaluate and share content on the Internet; will understand the impact and risks of digital technologies and the opportunities for society and culture; will know how to evaluate digital information and how to recognize false content.
2. Research skills
2.1. The student will be able to identify and define the problem of linguistic (interdisciplinary) research, select appropriate empirical material, research methods and bibliographic sources, formulate hypotheses and research questions.
2.2. The student will be able to analyse and organise material from a variety of sources, using modern information technologies, modern databases, language research tools, IoT platforms, current editions of literary texts, library resources, etc.
2.3. The student will be able to carry out linguistic (interdisciplinary) empirical research independently, using appropriate theoretical and methodological approaches; to analyse, interpret and describe data from natural and AI generated language in a coherent manner, to summarise the results of the research, and to formulate clear conclusions.
2.4. The student will be able to present his/her research in public, defend his/her views with arguments.
3. Special skills
3.1. The student will be able to express oneself fluently in English (C1 level), taking into account and critically evaluating the communicative intention, the addressee, the social and cultural context, etc.
3.2. The student will be able to produce and/or edit written, spoken and multimodal texts of various genres and types, taking into account cultural, social, stylistic and other contexts.
3.3. The student will be able to identify, analyse and evaluate problems related to the quality or relevance of AI-generated content, develop effective prompting strategies to guide AI solutions and to promote the generation of targeted (avoiding errors and inaccuracies) outputs that meet user needs.
3.4. The student having the knowledge of the subtleties of natural language will be able to use practical application of English phonetics, morphology, syntax and vocabulary to identify false or unethical content generated by AI, and to identify instances of psychological manipulation by social engineering.
3.5. The student will be able to analyse and organise the data used for AI training and to use language to model and evaluate AI systems to ensure their effectiveness and appropriateness.
4. University common competences
Collaboration:
4.1. The student will be able to articulate a common purpose beyond a single focus or worldview, to respectfully collaborate and share leadership.
Responsibility:
4.2. The student will be able to plan and rationally use time, resources, etc., in organising his/her own learning and work and takes personal responsibility for decisions regarding the management of time, other resources, etc.
4.3. The student will be able to take moral responsibility for his/her performance and its impact on society.
Interculturalism:
4.4. The student will be able to relate to people from different cultures, to show respect and empathy in conflict resolution, to express oneself clearly and to understand other people's communication, taking into account cultural differences.
Problem solving:
4.5. The student will be able to organise and critically evaluate information, provide arguments, identify and solve theoretical and practical problems in their field, both individually and in teams with specialists from different fields.
Openness to change:
4.6. The student will be able to analyse ideas and concepts using unconventional, innovative (or adaptive) methods, understanding the dynamics of the subject area and the value of systemic change.
4.7. The student will be able to think creatively and experiment with new formats of prompts, phrases and concepts to optimise AI performance.
5. Other social and personal skills
5.1. The student will be able to understand the ethical and legal principles involved in the use of AI technologies, especially in the area of language generation and processing, and the potential dangers of using AI, to apply these principles in academic and professional activities, and to take responsibility for his/her actions.
5.2. The student will be able to nderstand the value of lifelong learning for career success, personal development and the creation of social well-being.
Activities of teaching and learning:
The study programme uses traditional teaching methods (traditional theoretical lectures), active learning methods (problem-based learning, reflection, informal peer assessment, case studies, discussions, development of an AI assistant, summarising, developing algorithms or programs, designing a prototype of a system, role-play, etc.), interactive learning methods, research methods (e.g. a small-scale written work, research work in the oculography laboratory) and project-based teaching methods. IT programmes and tools are used when required. An important part of the studies is the students' independent work, which includes studying the literature, collecting and organising information, carrying out individual and group practical tasks, preparing presentations, preparing for examinations and defences, etc.
Methods of assessment of learning achievements:
A cumulative evaluation is used to assess the learning outcomes of study subjects (modules). The main methods of study assessment are tests (closed and open-ended, theoretical and practical questions), control works, presentations and their defences, team (creative) projects and their presentations, research papers and their presentations/defences, laboratory tasks, assessment of practical tasks performed individually or in a group, etc. The final assessment of most study subjects is a final examination.
Framework:
Study subjects (modules), practical training:
Linguistic subjects: Introduction to Linguistics, English Phonetics, English Morphology, English Syntax, English Lexicology, English Stylistics, Semantics, Pragmatics, Academic Writing, Argumentation, Sociolinguistics, Creative Linguistics, Corpus Linguistics, Cognitive Linguistics, Neurolinguistics.
Integrated Information Systems subjects: Fundamentals of Information Technology, Technology and the Digital Society, Fundamentals of Artificial Intelligence, Human-Robot Interaction (HRI) by Prompting and Information System Management, Data Extraction and Prompt Engineering, Technologies for Information Delivery Online, Practical No Code Programming, Natural Language Processing (NLP) and Neural Networks.
Interdisciplinary subjects: Experimental Linguistics, Social Media Discourse Analysis, AI platforms and Tools: Creative Projects.
The programme includes other subjects: Ethics and Law of Artificial Intelligence, Logic.
Professional practice and the Bachelor thesis belong to the group of Linguistic subjects.
Specialisations:
Optional courses:
Elective subjects: Psycholinguistics, Multimodal Communication, Professional Discourse Analysis, Language Perception, System and Structure, AI and Technology: An Introduction to Object-Oriented Programming, AI Assistants and Technologies, Practical AI Programming, Deep Learning, AI: Programming and Tools for Machine Learning, AI and ML Platforms, AI Principles of Information and Privacy Security, Digital Art Trends and Culture, AI Creativity and Innovation.
Individual study subjects. In semesters 2 and 4-7, students will be able to personalise their studies (30 credits) and freely choose from modules in philosophy, psychology, IT, cultural studies, philology, or other fields of study.
Distinctive features of a study programme:
The programme is conducted in English and is aimed at Lithuanian and foreign citizens interested in (English) linguistics and the application of DI.
The programme trains (English) language professionals and AI prompt engineers.
Access to professional activity or further study:
Access to professional activity:
The programme will prepare (English) language professionals who are competitive in the labour market and Generative Artificial Intelligence (GenAI) prompt engineers. Graduates will be able to successfully apply their knowledge of language and AI in a wide range of fields: research, security, politics, media, English language teaching (language courses), etc. With knowledge of (English) linguistics and the functioning and application of AI, and the ability to work analytically and creatively, they will develop meaningful and effective communication between human and machine, using specific prompts to help GenAI understand and perform the desired tasks. They will also be able to monitor machine learning, manage communication in social networks by applying the language skills they have acquired, evaluate and validate the content produced by the generative model, act as AI assistants and learning assistants.
Access to further study:
Opportunity to pursue an MA in the humanities
