Module: Introduction to Programming

Module Specifications

Academic Year 2026 - 2027

Module Title	Introduction to Programming
Module Code	TRA1025
Faculty	Humanities & Social Sciences	School	SALIS
NFQ level	9	Credit Rating	5

Description

This module provides a beginner-friendly introduction to programming for students with no prior coding experience, with an emphasis on practical applications in multilingual digital communication and natural language processing. Students learn how to install and run Python, write core Python programs, and build simple NLP pipelines. In the second half, the module explores AI-assisted programming and the role of generative AI in coding, including how to use agentic AI tools to plan, implement, test, and iterate on NLP-focused solutions. By the end of the module, students will be able to design and deploy a small AI-powered multilingual application and explain how they planned, implemented, evaluated, and documented it responsibly.

Learning Outcomes

1. Install and configure Python and core libraries, run scripts/apps, and use a beginner-appropriate development workflow (e.g., IDE/notebooks, virtual environments, package management).
2. Write basic Python programs using variables, control flow, functions, modules, data structures, and file I/O.
3. Manipulate and analyse language data in Python with attention to multilingual issues (encoding, segmentation, tokenisation, language differences).
4. Build and interpret foundational NLP pipelines using existing libraries (e.g., sentence splitting, POS tagging, NER), and evaluate outputs using simple, appropriate checks for multilingual use cases.
5. Prototype and compare simple language-technology components relevant to multilingual communication, including machine translation and language-model-based features (e.g., translation via APIs/models, prompting for transformation tasks such as summarisation or plain-language rewriting), and discuss limitations and quality risks at a beginner level.
6. Use generative AI tools to support programming tasks responsibly, while verifying outputs, documenting decisions, and respecting data/privacy boundaries.
7. Design, implement, and deploy a small AI-powered application for multilingual digital communication (e.g., MT-assisted workflow, multilingual text transformation, speech-to-speech prototype), and justify product decisions using concise process documentation.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	11	Concept sessions (programming + NLP + AI-assisted coding)
Laboratory	11	Guided coding labs + challenge-based studio
Assignment Completion	35	Mid-test prep + final project build/report
Independent learning	68	Practice, reading, debugging, iteration
Total Workload: 125

Section Breakdown
CRN	21459	Part of Term	Semester 2
Coursework	100%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator		Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
In Class Test	Students complete an in-person, time-limited practical coding test under controlled conditions. They are given small programming tasks aligned with the first half of the module (Python fundamentals + basic NLP manipulation). The tasks require writing and running working code.	30%	n/a
Assignment	Students build and deploy a small AI-powered web app for multilingual digital communication. The deliverables are: 1. Product Requirements Document (PRD): problem statement, target users, core use case, constraints, success criteria, and risk considerations. 2. Published app: deployed online, using a starter template provided in the module where appropriate. 3. Code repository: structured, readable code with minimal documentation. 4. Short report: explains how the solution was planned and implemented, what was tested/checked, limitations, and how AI tools were used responsibly during development (including verification practices and boundaries).	70%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

n/a
Cay S. Horstmann, Rance D. Necaise: 2018, Python For Everyone, 3rd, John Wiley & Sons, Limited,, 1119572819

Indicative Reading List

Books:
None

Articles:
None

Other Resources

None