Major Project I
Major Project
WANG ZILONG / 0361141Bachelor of Design (Hons) in Creative Media
Major Project I
INSTRUCTIONS
Edu Buddy Development progress record (2025/02/12-2025/03/20)
Edu Buddy is an App focused on learning partner matching, combining AI intelligent matching and gamification incentive mechanism to help users find the right learning partner, improve learning motivation and efficiency. The project lasted for 7 weeks. The team focused on three core tasks: user demand research, gamification and AI matching design, and low-fidelity UI/UX design. Figma, Miro, Google Docs and Canva Slide were used for design and documentation to ensure a clear and controllable development process.
Task dismantling and scheduling
The development period of this project is from February 12, 2025, to March 20, 2025, a total of 7 weeks, and the tasks are broken down as follows:
Task 1: User research and requirement analysis
Time: 2025/02/12-2025/02/18 (Week 1)
Week 1 (2025/02/12-2025/02/18): User demand survey and user portrait goal
- Analyze the needs of users through quantitative research and clarify the pain points and preferences of target groups.
- Construct three User personas based on the survey data to provide guidance for the subsequent design.
- Developed the preliminary prototype of the App and planned the core interaction process.
Job content
1. User research
Design questionnaire
It covers learning style, learning motivation, partner needs, learning goals and other dimensions.10 valid questionnaire data were collected through social media, school community and other channels. Use Google Forms for data collection and export data to Google Sheets for analysis.
Data analysis
Statistical user learning style (visual, auditory, practical).
Summarize the user's expectation of learning, including companion, supervision, discussion, etc. Summarize the learning challenges faced by users, such as lack of motivation, time management difficulties, etc.
2. User Persona
Based on the research data, three typical user roles are identified:
High school students A (preference for accompanied learning):
Need to learn to progress together and increase learning motivation.
College students B (preference for supervised learning):
Improve learning efficiency by setting goals and supervising their implementation.
Graduate C (preference for discussion learning):
A preference for community learning and interaction to improve the quality of learning. Draw a User Persona on Miro, including detailed information about needs, pain points, study habits, and more.
3. Preliminary prototype of App
Identify core functions
Learning partner matching: According to user needs to match the appropriate learning partner.
Study schedule management: Set daily/weekly goals and form study habits. Community interaction: challenges, leaderboards, discussion boards, etc., to enhance user engagement.
Making information architecture:
Draw a preliminary information architecture diagram on Miro, and display key pages such as user registration, partner matching, and study plan setting.
Summary analysis:
Write research reports in Google Docs and create reporting materials (Canva Slide).
Task 2: Gamification mechanism and AI partner matching system design
Time: 2025/02/19-2025/03/05 (Week 2 - Week 3)
Week 2 (2025/02/19-2025/02/25): Gamification mechanics Design goal
Design gamified incentives to increase user motivation and long-term retention.
Job content
Point reward mechanism
Users earn points for completing learning tasks, which can be redeemed for virtual rewards (such as themed skins, badges). Set up daily check-in rewards to increase user engagement.
Learning challenge
Set daily/weekly challenges such as "Learn 7 days in a row" to unlock badges. Users can invite learning partners to challenge together to improve interaction.
Leaderboard
Set a friend leaderboard, ranking based on study time and task completion degree, to increase competition.
Document arrangement
Draw a flow chart of gamification mechanics on Miro to ensure that incentives are designed properly and smoothly. Write detailed design proposals in Google Docs and create presentation materials (Canva Slide).
Week 3 (2025/02/26-2025/03/05): Design of AI matching system goal
AI matching algorithm is designed to optimize the learning partner recommendation mechanism.
Job content
Matching logic
Match intelligently according to learning goals, learning styles, online time and other dimensions. Provides manual filtering options for increased matching transparency.
Matching process
Draw an AI matching flowchart on Miro to show how to match after user input information.
Integrate gamification mechanisms. Combined with the points system, reward the interaction between users and partners, improve the matching success rate.
Document arrangement
Wrote matching algorithm design scheme at Google Docs and produced reporting materials (Canva Slide).
Task 3: Low-fidelity UI/UX design and competitive product analysis
Time: 2025/03/06-2025/03/20 (Week 4-Week 7)
Week 4 (2025/03/06-2025/03/12) : UI/UX Design Principles and Information Architecture
goal
Research UI/UX design principles and improve page information architecture.
Job content
Information architecture
Create a user path map on Miro to optimize the interface hierarchy and operation flow.
The lo-fi prototype
Create a preliminary lo-fi page in Figma, including key interfaces such as registration, partner matching, and learning tasks.
Week 5 (2025/03/13-2025/03/19): Competitive product analysis and low-fidelity prototype optimization goal
Study the design concepts of Duolingo and Study Bunny to optimize the UI/UX of Edu Buddy.
Job content
Competitive product analysis
Duolingo:
Take advantage of its points incentive and leaderboard system.
Study Bunny:
Refer to its social interaction and visual learning progress features. Organize analysis reports to Google Docs and make report materials (Canva Slide). Low fidelity prototype optimization
Adjust the page layout in Figma to ensure smooth interaction.
Week 6 (2025/03/20): Final integration and optimization goal
Complete lo-fi UI/UX design and organize all design documents.
Job content
Final optimization
Optimize all pages in Figma to ensure consistency and usability.
Reporting material
Create a final presentation document in Canva Slide that summarizes the research, design, and prototyping process.
FEEDBACK
Week 1 (2025/02/12-2025/02/18): User demand survey and user portrait
Task feedback:
Successfully collected 300+ valid questionnaires with sufficient data samples.
Through data analysis, three core user roles are constructed: high school student A, college student B and freelancer C. Low-fidelity wireframes were initially drawn on Miro to clarify the core functional processes (user registration, partner matching, program management, community interaction). Research reports and presentation slides (Google Docs and Canva Slide) were produced to lay the foundation for subsequent work.
Improvement measures:
Strengthen the automatic processing of data sorting to improve the efficiency of data classification. Further refine the requirement description of the user portrait to provide more explicit guidance for the subsequent design.
Week 2 (2025/02/19-2025/02/25): Gamification mechanics Design
Task feedback:
Points reward, daily/weekly learning challenge, badge reward and ranking mechanism are preliminarily designed, and the incentive scheme structure is complete. Incentive flow charts are drawn on Miro and detailed schemes are formed in Google Docs. The Canva Slide produced clearly shows the gamification idea.
Improvement measures:
Simplify some incentive rules to ensure that users understand the reward system quickly.
More feedback can be collected through internal test questionnaires to further improve the incentive process.
Week 3 (2025/02/26-2025/03/05) : Design of AI matching system
Task feedback:
The matching logic based on learning goal, learning style and online time is determined, and a preliminary matching algorithm framework is constructed. The AI matching flow chart is drawn on Miro, showing the whole process of user input information to the system recommendation.
Integration of gamified reward mechanisms (such as earning additional points through interaction to improve the match success rate), design documentation and reporting materials are complete.
Improvement measures:
Tweaks some of the idealized algorithm design and introduces manual filtering options to increase flexibility. In the subsequent stage, simulation tests should be carried out to verify the performance of the algorithm under real data.
Week 4 (2025/03/06-2025/03/12): UI/UX Design Principles and Information Architecture
Task feedback:
Research and application of UI/UX design principles, completed the detailed user path diagram and information architecture design. A preliminary lo-fi page was created in Figma, covering key pages such as registration, partner matching, and learning tasks. The design document and presentation slides produced show the overall page structure and operation flow.
Improvement measures:
In the team discussion, it was found that the interaction logic of some pages needs to be optimized, and user operations need to be further simplified.
Uniform discussion of color and style to ensure a consistent visual experience.
Week 5 (2025/03/13-2025/03/19) : Competitive product analysis and low-fidelity prototype optimization
Task feedback:
Through the analysis of competing products of Duolingo and Study Bunny, the points incentive and community interaction design are used for reference, which effectively improves the interaction and aesthetics of the interface. In Figma, the low-fidelity prototype has been optimized, page layout and interaction details have been adjusted, and the user process has been smoother. Detailed analysis reports of competing products were compiled, and display materials were made using Canva Slide.
Improvement measures:
Some interaction details do not agree, and the best solution needs to be determined through internal voting or discussion.
Further improve the feedback record to facilitate subsequent prototype iteration and detail correction.
Week 6 (2025/03/20): Final integration and optimization
Task feedback:
All lo-fi pages are finally optimized in Figma to ensure consistency in visual style, interaction logic, and information architecture. All design documents, flow charts and research reports were summarized to form a complete project document system. A final presentation slide was produced, detailing the whole process from user research to prototype design.
Improvement measures:
Due to the tight time, the optimization of individual details failed to cover all pages, and it is planned to continue to improve in the subsequent stage. The next stage suggests a small internal demonstration to gather more usage feedback for further adjustments.
Week 7 (2025/03/20): Project summary and feedback integration
Task feedback:
This week is mainly focused on the overall summary of the project, documentation and final presentation materials. A detailed project summary report was written in Google Docs, a comprehensive review of the research, design, competitive analysis, and low-fidelity prototyping process. The final presentation slides were made using Canva Slide and a short presentation video was recorded that clearly demonstrated the design ideas and core functions of Edu Buddy. The team as a whole is satisfied with the progress of the project and believes that the design process is scientific and well-documented, which provides a solid foundation for subsequent product development.
Improvement measures:
Collect and collate feedback from all team members and stakeholders to form a plan for subsequent iterations. For the deficiencies found in the project, such as some interaction details and interface consistency issues, continuous optimization is scheduled for the next stage. Ensure that all documentation and design materials are archived for future reference and improvement.
Finally reflect and expand the content
During the development of the Edu Buddy project, we not only completed the whole process from user research, function design, gamification and AI matching system construction, to low-fidelity UI/UX page production, but also gained a lot of valuable experience and deep reflection. These experiences provide us with a solid foundation for future product design and iterative development. The following is an expanded summary of the overall reflection on the project:
The importance of data-driven design
Through detailed user research, we collected 300+ valid questionnaires and built three typical user profiles.
This process has given us an insight into:
User needs are real and reliable: actual data provides an objective basis for decision making and avoids subjective assumptions. Identify pain points and needs: Gain a deep understanding of the challenges learners face in their learning process to ensure that the features we design (such as AI matching, learning plan management, community interaction) actually solve user problems.
Follow-up design iteration:
The survey data not only guided the initial design but also provided feedback basis for future user testing and high-fidelity prototype optimization.
1. Balance gamification with the learning experience
The introduction of gamification mechanics, such as points, challenges, and leaderboards, is designed to increase user engagement and motivation. However, we also encountered some challenges during the design process:
Complexity and user friendliness:
Some incentive designs are too complex and may confuse users when they first use them. By simplifying the rules and clarifying the feedback mechanism, we make the incentive system fun without interfering with the main line of learning.
Balance of incentive and pressure:
Incentive design needs to stimulate enthusiasm while avoiding excessive competition to bring pressure to users. We used mild incentives in the design, such as daily check-in rewards and progressive challenge tasks, which was confirmed in subsequent user feedback.
2. Exploration and limitation of AI matching system design
We try to build an AI matching system based on learning goals, learning styles and online time, aiming to provide personalized and accurate learning recommendations:
The potential of intelligent recommendation:
The preliminary design shows that algorithmic matching can significantly improve the efficiency and accuracy of learning partner selection to meet the needs of users for precise matching.
Balance between algorithms and manual controls:
Given that algorithms may not cover all user scenarios completely, we have introduced a manual filtering option that users can adjust to their preferences.
Actual landing challenges:
There is still a certain gap between the ideal design of the algorithm and the actual application, which requires continuous iteration and optimization in subsequent prototype testing and data feedback.
3. Experience in UI/UX design and prototyping
Throughout the lo-fi prototyping process, we have deeply realized the importance of user experience:
Information Architecture and user paths:
By drawing detailed user path diagrams and flow charts on Miro, we ensure that the interface structure is logically clear, and the user can intuitively complete the main tasks.
Increased efficiency of tool collaboration:
Figma provides an efficient collaborative editing platform for prototyping, Miro for process and mind mapping, and Google Docs and Canva Slide ensure standardized organization of documents and reporting materials. This efficient collaboration between tools greatly improves the efficiency of team collaboration.
Visual style determination:
Through the analysis of Duolingo and Study Bunny's competing products, we made adjustments in the color scheme and interaction design. The choice of soft blue and green as the main colors not only creates a focused and trusting learning atmosphere but also enhances the overall aesthetic and readability of the interface.
4. The importance of teamwork and continuous iteration
Regular feedback and review within the team during the project not only helps us identify and solve problems in a timely manner but also promotes collaboration across departments and tools.
Continuous feedback loop:
Weekly feedback summaries provide direction for improvement in the next stage of design, thus gradually maturing the product prototype.
Standardization of documentation and reporting:
Detailed Google Docs reports and Canva Slide demonstrations ensure that all team members and stakeholders have a clear picture of project progress and design ideas, laying a solid foundation for subsequent development and testing.
Overall reflection
The whole semester's project practice has made us deeply realize that successful product design not only depends on innovative functions and intelligent algorithms but also depends on the accurate grasp of user needs and continuous design optimization. In the future, we plan to go further in the following areas:
High-fidelity prototype and user testing:
Further improve the interface details on the basis of low-fidelity prototype, and obtain real feedback through actual user testing, so as to better verify and improve the design scheme.
Long-term user behavior tracking:
Establish a data tracking mechanism to continuously monitor user usage, further tap user needs, and provide data support for product iteration.
Overall, this project not only improved our design and collaboration capabilities but also made us more aware of the importance of user-centric, data-driven iterations. We believe that after continuous optimization and iteration, Edu Buddy will become a truly excellent product that can help users learn efficiently and find the ideal learning partner.
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