CH2: VILNIUS TECH - AI-Driven Alumni Information

🎓 Challenge 2 - VILNIUS TECH: AI-Driven Alumni Information System for Educational Insights

• Problem Statement: Manually gathering career data from alumni across multiple social networks is time-consuming and labor-intensive. Educational institutions need automated solutions to track alumni success effectively.
• Challenge: Build an AI-powered system to collect and analyze alumni career data from LinkedIn, helping educational institutions understand and showcase the success of their graduates.
• Link to Partner Challenges Document
• Partner: Vilnus Tech (Vilnius Gediminas Technical University) - Website
• Challange Expert -  Simona Ramanauskaitė

Theme:Education - AI applications for personalised learning, intelligent tutoring, and educational support.

Challenge Title:AI-Powered Alumni Insights System for Enhanced Educational Outcomes

Problem Statement:Educational institutions often promote their programs and courses, but the true measure of success lies in the career trajectories of their alumni. However, manually gathering comprehensive data about graduates' careers from various sources, such as LinkedIn and other social networks, is labour-intensive and time-consuming. There is a need for an automated system that can efficiently collect, analyze, and present this data, providing valuable insights into the effectiveness of different educational programs and courses.

Challenge Objective:Propose an AI-driven solution that develops an innovative Alumni Information System. This system should automate data collection from LinkedIn and other social networks, linking it with the respective educational institutions. The integrated data should then be analysed and presented using a Large Language Model (LLM) engine, enabling advanced, flexible text queries to provide insights into graduates' career trajectories.

Expected Outcomes:Participants are expected to create a solution that automates the data collection process, integrates it with educational institution databases, and utilises an LLM engine to generate user-friendly reports and insights. Possible outcomes include:

• Enhanced Visibility of Study Benefits: Provide detailed insights into how different programs and courses have contributed to alumni career success.
• Realistic Graduate Opportunities: Offer a realistic view of the job market and career opportunities available to graduates from specific institutions.
• Education Institution Promotion: Highlight the success stories of alumni to attract prospective students and promote the educational institution.

Data Provided by Partner:Participants must prepare datasets by collecting data from LinkedIn and other social networks. A sample data set is available for reference: Alumni Data Sample.

Data Restrictions:Participants should focus on structured and unstructured text data collected from social networks. The challenge does not involve image, audio, or video processing.

Support and Resources:Participants will have access to mentors from VILNIUS TECH, who can provide insights into educational data integration and assist with technical challenges related to implementing LLMs.

Rewards and Recognition:Winning solutions may be considered for implementation by VILNIUS TECH to enhance its promotional efforts and provide prospective students with valuable information. Additionally, participants will receive recognition through certificates, public announcements, and opportunities for further collaboration with educational institutions.

Challenge Expert/Contact Person:For any questions or further information, please contact Simona Ramanauskaitė, Professor at VILNIUS TECH.Email: Ask OrganisersWebsite: vilniustech.lt