CSYM015 Intelligent Systems L7 Assignment 2 Brief | UON

Learning Outcomes of CSYM015

Aim: This assessment aims to provide students with the opportunity to design, develop, and critically evaluate an advanced AI-based application that contributes to positive social impact. Aligned with the theme of Pioneering Social Enhancement Through AI Solutions, this project challenges students to address meaningful real-world problems, such as those related to health, education, accessibility, safety, or sustainability—using intelligent systems.

Students will apply advanced AI methodologies, including computer vision, speech recognition, image processing, or natural language processing, while gaining hands-on experience with data-driven problem-solving. The assessment promotes the development of technically robust, ethically responsible, and socially conscious AI solutions, enabling students to demonstrate critical analysis, innovation, and changemaker thinking in support of community and global well-being.

Subject-Specific Knowledge, Understanding & Application

• Summarise the theoretical background that underpins the development of intelligent systems.
• Differentiate between the various artificial intelligence methods.
• Critically appraise the various artificial intelligence techniques, considering their appropriateness, advantages and disadvantages in specific applications
• Devise a range of typical applications using artificial intelligence methods.

Changemaker & Employability Skills

• Self-management: monitor and control their learning process;
• Problem solving: develop an extensive range of skills for the construction of intelligent systems;

To achieve this, you will need to:

• Identify a problem that can be effectively solved using AI methodologies.
• Conduct thorough research to understand existing solutions and gaps in the field.
• Select appropriate datasets and preprocess them for training and evaluation.
• Choose suitable AI models and justify their application.
• Develop an AI-based application that addresses the identified problem.
• Implement and evaluate the model using appropriate performance metrics.
• Document the entire development process in a structured technical report.
• Present findings, analysis, and demonstrations through video submission.

Assessment Overview

This assignment supports the achievement of all specified learning outcomes (a–f), as detailed in the module specification. Students must complete all tasks individually to demonstrate subject-specific knowledge, practical application, and changemaker skills as part of this Level 7 module.

Technical Report (4000 Words):

The report should include the following sections:

1. Introduction:

• Define the problem and explain its significance.
• Justify the choice of AI techniques and the project’s objectives.

2. Literature Review:

• Discuss existing solutions, models, and technologies related to the project.
• Compare different methodologies and highlight their advantages and limitations.

3. Methodology and AI Model Design:

• Provide a step-by-step breakdown of the AI techniques used.
• Explain dataset selection, preprocessing steps, and feature extraction methods.
•  Detail the architecture and algorithms implemented.

4. Implementation and Technical Execution:

• Provide code snippets, flowcharts, and diagrams to illustrate the implementation process.
• Describe how input data is transformed through each stage of the system pipeline, from preprocessing to model inference, including decision logic and AI integration.
• Describe hardware/software dependencies and any libraries used.

5. Model Evaluation and Critical Analysis:

• Measure the system’s performance using appropriate metrics (e.g., accuracy,
  precision, recall, F1-score).
• Compare results with existing approaches and justify improvements.
• Provide error analysis and discuss limitations.

6. Ethical Considerations:

• Address potential biases in training data.
• Discuss privacy concerns, security vulnerabilities, and responsible AI usage.

7. Conclusion and Future Work:

• Summarise key findings and contributions.
• Suggest potential improvements and future extensions of the project.

Project Development:

• Develop and submit a fully functional AI application aligned with the chosen domain.
• Ensure proper documentation of code, methodology, and dataset usage.
• Submit relevant files, including source code, datasets (if applicable), and a working demo.

Viva/Demo Presentation:

In addition to the report, you must submit a video demo (URL) of your assignment. The demo should be about 10 minutes long and should logically cover all your work. You should explain the main phases of design and implementation, covering the main fragments of code. Your face and voice need to be clear in the video. It should also include a walkthrough of using the software and must demonstrate the key features. The module tutor reserves the right to invite you for an online viva voce. Poor demo/viva could negatively influence other sections in the marking criteria and may result in an overall fail grade. You may also be referred for a suspected academic misconduct investigation.

Assessment Focus

• Functionality: Does the project meet the intended objectives, and does it operate correctly?
• Technical Complexity: Does the implementation demonstrate depth and appropriate difficulty for Level 7?
• Innovation and Creativity: Does the project introduce novel elements or refine existing AI techniques?
• Evaluation and Justification: Are the results analysed thoroughly, and justifications provided for methodological choices?
• Report Quality: Is the report well-structured, detailed, and technically sound?

Assessment Tasks

Title: Pioneering Social Enhancement Through AI Solutions

You are required to research, design, develop, and evaluate an advanced AI-based application that directly contributes to social betterment or community empowerment. Your project must address a relevant societal challenge using intelligent system methodologies and demonstrate how AI can ethically and responsibly enhance human lives. Your work will be presented through a comprehensive technical report, a functional AI prototype, and a video presentation. The project should reflect the core principles of ethical AI, social impact, and technical excellence.

Assignment Objectives

Your project must:

• Tackle a real-world issue affecting individuals, communities, or vulnerable populations.
• Apply advanced AI techniques to offer a meaningful, scalable, and socially responsible solution.
• Demonstrate mastery of the technical foundations of intelligent systems.
• Evaluate performance, impact, and ethical implications of your solution.
• Showcase a commitment to the Changemaker ethos of using innovation to drive positive change

Core Tasks

1. Problem Definition and Social Relevance

• Identify a current issue (local or global) that negatively impacts a community or population.
• Justify the problem’s social importance and your project’s potential to enhance lives.
• Align the problem with UN Sustainable Development Goals (SDGs) or social innovation themes.

2. Research and Theoretical Grounding

• Conduct a literature review of existing AI-based approaches to this or similar problems.
• Compare models and discuss their limitations in addressing real-world social needs.

3. Dataset Selection and Preprocessing

• Identify an open-source or custom dataset relevant to your problem.
• Describe preprocessing steps, and address fairness, bias, and data privacy concerns.

4. Model Design and Development

• Choose appropriate AI techniques (e.g., NLP, computer vision, deep learning).
• Justify model selection and implementation strategy.
• Include model architecture, algorithm flowcharts, training setup, and tools used.

5. Functional Prototype

• Develop a working system capable of solving the identified problem.
• Ensure usability, robustness, and alignment with ethical AI practices.

6. Performance Evaluation

• Use relevant metrics (e.g., accuracy, F1-score, AUC, BLEU score).
• Compare performance with existing benchmarks or real-world standards.
• Discuss system reliability, fairness, and edge cases.

7. Ethical Reflection and Social Impact

• Critically assess potential biases, risks, and ethical challenges.
• Reflect on how your system promotes inclusivity, trust, and social good.
•  Consider how the system could be scaled responsibly.

8. Conclusion and Future Vision

• Summarise your key findings and technical contributions.
• Propose future developments or deployment pathways for real-world use.