COMM119 | 2025/6 | University of Exeter

MODULE TITLE	AI in Environment	CREDIT VALUE	15
MODULE CODE	COMM119	MODULE CONVENER	Unknown

DURATION: TERM	1	2	3
DURATION: WEEKS		11

Number of Students Taking Module (anticipated)	40

DESCRIPTION - summary of the module content

AI is playing a crucial role in addressing environmental challenges by enabling data-driven decision-making and sustainable solutions. In this module, you will study the fundamental concepts of AI and its applications in environmental problems, including for example, geographical data analysis, machine learning, and foundation models for climate. You will study the applications of AI in environment, such as climate change mitigation and biodiversity conservation. You will attend lectures complemented by lab sessions or discussion, where you will apply AI techniques to real-world environmental datasets and analyse case studies. This module is suitable for Computer Science, Mathematics and Engineering students and any students with experience in programming and fundamental machine learning concepts.

AIMS - intentions of the module

This module aims to equip you with the knowledge and skills to apply AI technologies in addressing environmental challenges. You will explore key concepts, for example, machine learning, computer vision, and time-series data analysis, and examine their applications such as environmental monitoring and climate modelling. You will learn to analyse real-world challenges, such as weather forecasting, disaster management, and pollution monitoring, and formulate them as machine learning problems. Additionally, you will assess the performance and impact of AI solutions across various environmental applications, considering both technological and ethical implications. Through hands-on projects and case studies, you will gain practical experience in developing AI-driven solutions for environmental challenges, preparing you for careers in environment-related research, policy-making, or industry applications.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Explain the fundamental concepts of AI and its applications in environmental problems, including for example, machine learning and computer vision in areas such as climate change, biodiversity conservation, and pollution control.
2. Formulate real-world environmental challenges as machine learning problems.

3. Design AI-based solutions to address environmental challenges, such as weather forecasting, disaster management, and ecosystem monitoring.

4. Evaluate the designed AI solutions for limitations, strengths, improvements required, etc.

Discipline Specific Skills and Knowledge

5. Critically analyse and evaluate the performance and impact of AI in various applications.
6. Identify the compromises and trade-offs which must be made when translating AI theory into practice.

Personal and Key Transferable / Employment Skills and Knowledge

7. Effectively communicate insights and evaluations drawn from research papers.

SYLLABUS PLAN - summary of the structure and academic content of the module

Concepts and Theoretical Foundations

Introduction to AI for environmental challenges.

Introduction to deep learning and advanced model structures.

Data sources and challenges in environmental AI.

Implementation and Practical Techniques

For example, time-series analysis for climate prediction and computer vision for environmental applications.

Analysis and Evaluation

Quantitative and qualitative evaluation techniques in AI for environment.

Ablation study.

Real-world case studies and advanced applications.

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	33	Guided Independent Study	117	Placement / Study Abroad

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category	Hours of study time	Description
Scheduled Learning & Teaching activities	22	Lectures
Scheduled Learning & Teaching activities	11	Workshops/tutorials
Guided independent study	45	Coursework preparation and completion
Guided independent study	72	Wider reading and self-study

ASSESSMENT

FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Form of Assessment	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Practical Exercises	10	All	Answers to exercises and oral feedback

SUMMATIVE ASSESSMENT (% of credit)

Coursework	30	Written Exams	70	Practical Exams

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of Credit	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Continuous assessment	30	30 hours	All	Written
Written exam – closed book	70	2 hours	1, 3, 4, 5, 6	Written

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)

Original Form of Assessment	Form of Re-assessment	ILOs Re-assessed	Time Scale for Re-assessment
Continuous assessment	Continuous assessment	All	Ref/Def period
Written exam – closed book	Written exam – closed book	1, 3, 4, 5, 6	Ref/Def period

RE-ASSESSMENT NOTES

Reassessment will be by coursework/quiz in the failed or deferred element only. For referred candidates, the module mark will be capped at 50%. For deferred candidates, the module mark will be uncapped.

RESOURCES

INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

• Kumari, N. and Pandey, S., 2023. Application of artificial intelligence in environmental sustainability and climate change. In Visualization techniques for climate change with machine learning and artificial intelligence (pp. 293-316). Elsevier.

• Nishant, R., Kennedy, M. and Corbett, J., 2020. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International journal of information management, 53, p.102104.

• Srivastava, A.N., Nemani, R. and Steinhaeuser, K. eds., 2017. Large-scale machine learning in the earth sciences. CRC Press.

• Hassanien, A.E., Darwish, A. and Elghamrawy, S.M. eds., 2024. Artificial Intelligence for Environmental Sustainability and Green Initiatives. Springer Nature Switzerland, Imprint: Springer.

• Silvestro, D., Goria, S., Sterner, T. and Antonelli, A., 2022. Improving biodiversity protection through artificial intelligence. Nature sustainability, 5(5), pp.415-424.

• Fan, Z., Yan, Z. and Wen, S., 2023. Deep learning and artificial intelligence in sustainability: a review of SDGs, renewable energy, and environmental health. Sustainability, 15(18), p.13493.

Web based and Electronic Resources:

ELE – Faculty to provide hyperlink to appropriate pages

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	COMM113
CO-REQUISITE MODULES

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Monday 11th November 2024	LAST REVISION DATE	Wednesday 6th August 2025

KEY WORDS SEARCH	Environmental intelligence, AI, machine learning

AI in Environment - 2025 entry