AI-Driven Mentorhip Facilitator

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Introduction

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Existing solutions

Despite the persistent issue, numerous platforms offer mentorship and networking capabilities. Among them, Growth Mentor stands out as a popular choice, connecting startup founders with industry leaders for personalized growth and marketing discussions [1]. However, a key drawback shared by these platforms is their industry-specific focus, coupled with increasing subscription fees. Furthermore, users must invest time in manually selecting mentors without guidance or support in the process.

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Problem statement

In the fast-paced world we live in, keeping up with industry changes and learning new skills daily is key. Having a solid mentor by your side to guide you through challenges or just offer a shoulder to lean on is crucial. But let's be real, finding that perfect mentor ain't easy. So, we are on a mission to shake things up and figure out how to hook up padawans with the right Masters. 'Let AI be with us'

Target audience

As fellow students, we possess a profound understanding of the hurdles faced by aspiring professionals on their journey to seniority in their respective fields. Consequently, we have identified students and interns aged 18 to 27
as our primary target audience. To gain deeper insights into their experiences and challenges, we've undertaken thorough user research within this demographic.

ANALYSIS OF SIMILAR PLATFORMS

we analysed top-used platforms for mentorship and networking.
There are some key issues they have.

Difficulty Approaching Potential Mentors

The platform doesn't have a structured mentorship program, which can make users unsure about how to reach out to potential mentors

LinkedIn

Lack of Relevant Connections

On some professional networking platforms that utilize a swiping mechanism for connecting users, there is a potential issue where the method may not consistently yield relevant professional connections. The swiping process, which often relies on quick profile assessments, may not adequately address the detailed professional needs and nuances of users. As a result, the connections made might not align with users' specific career goals or industry requirements

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Bumble Bizz

Time Constraints

Active participation in certain apps, such as engaging in discussions, reading the latest research, and establishing connections, requires a considerable investment of time, which not all users can afford

ResearchGate

User Research: Overall Statistics

11
Participants
74%
Biweekly or rarely
72%
Want to network more often
TOP 3
Mentor Qualities Identified

user Research

more than 10 potential users were surveyed regarding their experience and desires for a mentor-matching platform that best suits them.
Users' demand
key qualities
Challenges
Mentors' PerSpective

Survey results demonstrate a demand for a platform like ours

  • 72% of people also have shown interest in an AI-driven networking facilitator
  • About 74% of people do not engage in professional networking activities more often than biweekly.
  • People want to network, but currently do not do it very often, demonstrating a demand and desire that aligns with our app's goals.

Important qualities users look for when choosing a mentor

  • Relevant expertise or education, career position and matching achievements to goals are the three most important qualities people look for when choosing an appropriate mentor
    • The platform we build should be able to match candidates with those qualities prioritized
    • These three qualities can be related by the professional pedigree of the mentor, where candidates value similar career prospects and evaluate the level of success by their career position
      • People look for mentors who have achieved high career positions in areas that they themselves want to pursue
      • Users want to 'follow their footsteps'

Current challenges our app will address.

  • The biggest challenges we want our platform to address are finding relevant connections and giving users tips on how to approach potential mentors
    • The app itself can become the liaison for users to connect to mentors
    • Communication tips can be built-in to the chat tool so that users can be given prompts to help them make the first move in connecting with potential mentors
  • Relevant connections can be the algorithm that pushes some mentors to the top of their recommended page
    • Relevance can be evaluated through the educational background, CV submissions, keyword matching, career goals and personal interests

Mentors communicated similar to mentees' desires and challenges

  • Mentors synonymously agreed that the main challenge was fostering meaningful connections between their mentees and potential contacts, as a result of insufficient follow-up opportunities
    • This can be addressed with the App offering scheduled check-ins to encourage long-term support
    • Integrating the App with LinkedIn to suggest events would also facilitate mentees in fostering new connections.
  • Similar to mentees, mentors valued an alignment of expertise and experience to the mentee's goals
  • Analysis of mentor vs mentee goals tells us that the app interface does not need to differ greatly for the type of user they are, as it is likely that a mentee can also act as a mentor for someone else
    • The app should focus on communication elements that can be universal for whatever role the user plays

COGNITIVE ERGONOMIC PRINCIPLES

The Core Principles Underpinning Our Design

Reduce Stress

Our cognitive ergonomic design aims to create interface and environment that minimize stress levels for users, promoting a more comfortable and productive experience.

Assess Workload

It involves evaluating the cognitive demands placed on users during tasks to ensure they remain within manageable limits, preventing overload and maintaining efficiency.

Minimize Working Memory

With this principle we emphasized designing interface and workflows that reduce the need for users to hold information in their working memory, thus enhancing cognitive ease and task performance.

Prototype

Our Ideas and their realization

Low-fidelity prototype

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We sketched our ideas on paper for our low fidelity prototype. After some internal testing and asking around with potential users, we decided to proceed with the flow to higher fidelity prototype.

The flow consists of:
‍
‍1. Uploading your information
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2. Run AI matching to see who matches to be a potential mentor
‍
3. See mentor's information and AI recommended topics
‍
4. Start conversation with your mentor
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high-fidelity prototype link:
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Try our Prototype


Feature breakdown for high-fidelity prototype
‍

Feature I AI generated ice-breaker prompts

Target Pain Point: People might not know where to start conversations, especially with mentors who they have not known very well.
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Explanations: Blocks in black background would be the AI features on how the mentor and the mentee would fit.
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Feature II Matching people with high relevance

Target Pain Point: From survey we know that People might not know where to find the right connection. Hence, we give them a way to meet new mentors based on the information they put in.

Explanation: Users would input information like the right image and get recommendations for mentors on the right screen.
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Feature III Easy knowledge retrieval of mentor/mentee background

Target Pain Point: People tend to forget who each person is from at group networking events. Hence, this will reduce their mental workload by giving the users more information and letting them recognize who they met instead of recalling.

Explanation: Allow displaying information on chat screen for a better recall.
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user Testing

We have tested our solution with 6 users via usability testing

We have mainly recruited from University of Waterloo students as they fit the demographic of our potential users as users with networking needs and may seek advice from higher years.

Overall STATISTIcS

6
PARTICIPANTS
95%
CLICK ACCURACY
6.5
SEQ SCORE
BENCHMARK: 5.5
4/6
PARTICIPANTS 
‍GIVES 9+ IN NPS SCORE
5/6
PARTICIPANTS
‍PREFER OUR APP
OVER COMPETITOR

Comparing Prototype with Competitors

Competitors

Praxeum

Generalized networking experience
Networking experience more tailored to university students in finding mentors
The need to find mentors on different platforms for each subject
Able to find mentors for study, career, and interest in one place.
Seemingly hard and scary experience to start a chat
AI powered ice breaker questions
Network based on people in your organization
Network based on people that might be a great fit

Conclusion

Here we make a reflection on what was done, what can be improved and
the future work

What we have done

In line with our chosen topic, we conducted user research among the platform's target demographic: students and interns aged 18 to 27 who encounter difficulties in connecting with mentors to enhance their skills. Drawing from the findings, we crafted a low-fidelity prototype and personally tested it, identifying pivotal pain points. Utilizing this feedback, we iterated and crafted a high-fidelity prototype for our platform, dubbed 'Praxeum'. This iteration underwent testing with 6 potential users, yielding promising results. Feedback was overwhelmingly positive, with our platform receiving high ratings from participants.

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What we could improve

During user testing, valuable insights emerged, highlighting areas for improvement. These included the necessity to integrate a search field, facilitating users in locating individuals. Additionally, there was a call for validating mentors' skills, enhancing the platform's credibility. Users also expressed a desire for functionality allowing them to track their progress effectively. Another suggestion was to incorporate a menu button for easy access to personal profiles. Furthermore, enhancing visual differentiation between tabs was deemed beneficial for user navigation. To foster connectivity, participants recommended the addition of a 'connect' button and the option to build one's own network, fostering a more dynamic user experience.

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Feedback reflection

Feedback given from the presentation allowed us to think critically of how the app would differ for users depending on their personal goals. When considering mentor vs mentee interface, through user research we found that the app should not need to differ greatly due to users having similar goals and deal with similar struggles. Instead we could add badges to individual conversations that labels the role of each user within the relationship. The wireframe although simplistic, our team synonymously agreed with the direction that the app was going and wanted to prioritize prototyping to ensure positive user-testing results.

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Future of the platfrom

Moving forward, 'Praxeum' is poised for dynamic growth and innovation. Building on user insights, our focus is on expanding features, refining mentor-mentee matching, and fostering a vibrant community. Personalization will be key, with tailored profiles and recommendations. Usability enhancements will ensure a seamless experience. Our commitment to innovation ensures 'Praxeum' remains a valuable resource for skill enhancement, mentorship, and personal growth.

our team

Appendix

Research survey & result

We sketched our ideas on paper for our low fidelity prototype. After some internal testing and asking around with potential users, we decided to proceed with the flow to higher fidelity prototype.

The flow consists of:
1. Uploading your information
2. Run AI matching to see who matches to be a potential mentor
3. See mentor's information and AI recommended topics.
4. Start conversation with your mentor.
‍

Learn more

User testing post-testing survey & result