Science Communications MSc
Generative AI in Scientific Communications BIOL5439
- Academic Session: 2026-27
- School: MVLS College Services
- Credits: 20
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 1
- Available to Visiting Students: No
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
The world is becoming augmented by artificial intelligence (AI). Generative AI (GenAI) is now a part of our everyday lives. When prompted, large language models and AI image generators can rapidly generate specific text, image, or video responses. This course will introduce students to the use of LLMs and AI image generators in scientific communications and provide frameworks about how to use these technologies appropriately, responsibly and ethically.
Timetable
This course will consist of lectures and workshops delivered over 5 weeks in semester 1.
Excluded Courses
None
Co-requisites
None
Assessment
1. Group Crisis Response Simulation (1000 words) - ILOs 1 & 2 (25%)
2. Individual Production of Information Pack Using GenAI (1500 words) - ILOs 1-4 (60%)
3. Individual Reflective Analysis of AI usage (500 words) - ILO 4 (15%)
Are reassessment opportunities available for all summative assessments? No
It is the default expectation that all courses will offer opportunities for reassessment or deferred assessment. Where it is not possible to offer this in some assessment components, the grade achieved at the first attempt will be counted towards the final course grade, and any exceptions for this course are described below.
[No exceptions]
Course Aims
This course aims to provide students with skills and knowledge to effectively use GenAI for generating scientific information and imagery. Students will learn how to effectively engineer prompts to generate responses and images and consider the scientific integrity and reliability of responses.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
1. Critically evaluate the use of effective prompt engineering for generating scientific responses and images by LLMs and AI image generators, respectively, for intended audiences.
2. Critically appraise the scientific integrity and reliability of responses generated by LLMs and how this relates to published, peer reviewed scientific literature.
3. Critically assess the presence of bias in GenAI platforms and why these biases occur.
4. Critically evaluate the regulations and ethical considerations surrounding the use of GenAI technology in scientific research and communication