[Briefly describe the purpose of the project, the problem it solves, and its value to users.]
- Link to User Stories in GitHub Projects:
- [Add a link to the GitHub Projects kanban board.]
- Wireframes:
- [Attach or link to accessible wireframes used in the design process, ensuring high colour contrast and alt text for visual elements.]
- [Explain the rationale behind the layout and design choices, focusing on usability and accessibility for all users, including those using assistive technologies.]
- Design Rationale:
- [Explain key design decisions, such as layout, colour scheme, typography, and how accessibility guidelines (e.g., WCAG) were integrated.]
- [Highlight any considerations made for users with disabilities, such as screen reader support.]
- Reasoning For Any Final Changes:
- [Summarise significant changes made to the design during development and the reasons behind them.]
- [Reflect on how these changes enhance inclusivity and accessibility.]
- Feature 1: [Briefly describe the implemented feature.]
- Feature 2: [Briefly describe the implemented feature.]
- Inclusivity Notes:
- [Mention how the features address the needs of diverse users, including those with SEND.]
- Platform: [Platform used, e.g., Heroku, AWS, etc.]
- High-Level Deployment Steps:
- [Step 1]
- [Step 2]
- [Step 3]
- Verification and Validation:
- Steps taken to verify the deployed version matches the development version in functionality.
- [Include any additional checks to ensure accessibility of the deployed application.]
- Security Measures:
- Use of environment variables for sensitive data.
- Ensured DEBUG mode is disabled in production.
(Highlight how prompts, such as reverse, question-and-answer or multi-step, were used to support learners with SEND or ALN where relevant.)
-
Code Creation:
- Reflection: Strategic use of AI allowed for rapid prototyping, with minor adjustments for alignment with project goals.
- Examples: Reverse prompts for alternative code solutions and question-answer prompts for resolving specific challenges.
-
Debugging:
- Reflection: Key interventions included resolving logic errors and enhancing maintainability, with a focus on simplifying complex logic to make it accessible.
-
Performance and UX Optimization:
- Reflection: Minimal manual adjustments were needed to apply AI-driven improvements, which enhanced application speed and user experience for all users.
-
Automated Unit Testing: (If undertaken)
- Reflection: Adjustments were made to improve test coverage and ensure alignment with functionality. Prompts were used to generate inclusive test cases that considered edge cases for accessibility.
-
Overall Impact:
- AI tools streamlined repetitive tasks, enabling focus on high-level development.
- Efficiency gains included faster debugging, comprehensive testing, and improved code quality.
- Challenges included contextual adjustments to AI-generated outputs, which were resolved effectively, enhancing inclusivity.
- Manual Testing:
- Devices and Browsers Tested: [List devices and browsers, ensuring testing was conducted with assistive technologies such as screen readers or keyboard-only navigation.]
- Features Tested: [Summarise features tested manually, e.g., CRUD operations, navigation.]
- Results: [Summarise testing results, e.g., "All critical features worked as expected, including accessibility checks."]
- Automated Testing: (If undertaken)
- Tools Used: [Mention any testing frameworks or tools, e.g., Django TestCase.]
- Features Covered: [Briefly list features covered by automated tests.]
- Adjustments Made: [Describe any manual corrections to AI-generated test cases, particularly for accessibility.]
- [List potential improvements or additional features for future development.]
- Consider enhancements to improve accessibility further, such as voice input capabilities or additional language support.