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IS41720

Academic Year 2026/2027

Language Models and Methods (IS41720)

Subject:
Information Studies
College:
Social Sciences & Law
School:
Information & Comms Studies
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Courtney Ford
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module explores the principles and applications of language models, such as GPT and BERT, within the field of Information and Communication Studies. Students will gain a foundational understanding of natural language processing (NLP) and the role of language models in analysing and generating text, with a focus on their impact on tasks such as summarisation, classification, and conversational systems.

Through both theoretical and practical applications, the module examines how language models are applied to domains such as library studies, information systems, and digital policy. By the end of the module, students will be equipped to critically evaluate and apply language models to real-world challenges relevant to Information and Communication Studies.

About this Module

Learning Outcomes:

On successful completion of this module, students should be able to:

1. Demonstrate an understanding of language models and their applications in Information and Communication Studies.
2. Apply language models to practical tasks and real-world applications in library studies, information systems, and digital policy.
3. Develop technical and conceptual skills in text data and natural language processing.
4. Critically evaluate ethical issues such as bias, transparency, and equitable access in language models.

Indicative Module Content:

- Foundations of language models
- Language model applications in Information and Communication Studies
- Working with text data
- Ethical and societal implications of language models
- Practical approaches to designing and evaluating tools that leverage language models

UNESCO highlights a set of key competencies that support individuals in their development and support society in achieving the UN Sustainable Development Goals (SDGs). UCD has adapted these competencies and is combining them with others to form a general framework of learning competencies. This module will help you develop the competencies below to the levels specified. 
Learning Competency Additional Information Level

Digital Literacy and Judgement

 The ability to access, evaluate, create and communicate information in digital environments; to engage critically, ethically and responsibly with digital technologies and digital information; to understand their opportunities, limitations, risks and impact on individual’s digital identities; and to exercise informed judgement in digital participation and decision-making. Proficient

AI Literacy & Agency

 The ability to understand, critically evaluate and responsibly engage with artificial intelligence; to recognise how AI systems are shaped by human values; to assess their ethical, social and environmental implications; and to exercise human judgement, agency and accountability in AI-related contexts. Proficient

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


 

Student Effort Hours:
Student Effort Type Hours
Lectures

12
Practical

12
Autonomous Student Learning

100
Total

124

Approaches to Teaching and Learning:
The module uses a combination of teaching and learning methods to support students in developing both theoretical knowledge and practical skills, including:

Lectures, active/task-based learning, case-based learning, peer and group work, and reflective learning.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Group Work Assignment: Language Model Project Week 15 Graded No
60
 No
Exam (In-person): Midterm exam Week 7 Graded No
40
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

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