Discovery Sprint Plan developed during DPI 678MB: Product Management and Society at the Harvard Kennedy School with Prof. Kathy Pham.
This project represents student work; no association with Discovery Education.
Project Synopsis
The recent public availability of ChatGPT and other generative AI has made headlines around the world, with particular attention to its potentially disruptive impacts on education.
Discovery Education (DE) is a digital educational platform that provides dashboards, interactive textbooks, on-demand teacher resources, professional development, and more. DE serves 4.5 million educators and over 50 million students.
DE already incorporates machine learning into its teacher-facing user interface (UI) to serve up personalized recommendations (powered by Amazon Web Services) and offers Microsoft’s Immersive Reader (powered by Azure Applied AI Services) to improve reading comprehension for students.
A discovery sprint is a time-bound way of understanding a problem and testing potential solutions. I developed a hypothetical discovery sprint plan for DE to explore the opportunity of generative AI, including problem statement, team members, questions, stakeholders, deliverable outline, and day-to-day schedule.
Discovery Sprint Team
Senior Director, Product Management
Brings expertise in leadership as well as high-level knowledge of DE’s product lines. Experienced in developing product roadmaps, creating business cases, and driving adoption and engagement.
The senior director will act as the decider during the sprint.
Software Engineer
Brings technical skills and knowledge of DE’s back-end requirements as well as experience from launching DE’s existing machine learning for teacher recommendations in collaboration on Amazon Web Services (AWS).
Instructional Designer
Brings expertise in defining learning objectives and designing engaging learning experiences.
Experienced in collaborating on cross-functional teams (subject matter experts, production, UX research, learning analytics, etc.) to align learning objectives, content, and delivery methods.
The instructional designer will also act as the facilitator for group ideation and prototyping.
User Experience Lead
Brings expertise in both design thinking and user research. Critical for understanding user needs and behavior. Experienced in conducting user tests and interviews, analyzing data, and creating wireframes and other prototypes that communicate product vision.
2 Product Managers
Bring in-depth product knowledge and vision for two core product offerings: (1) “Techbook” digital textbooks for science, social studies, and math; and (2) “Lessons and activities” product category which includes teacher tools such as the “Studio” function, classroom activities, and digital student interactives.
Content Marketing Specialist
Brings communication, graphic design, and copywriting skills. Provides expertise in identifying target audiences, understanding customer pain points, and effectively communicating the value of DE’s products.
Legal Counsel (part-time member)
Because of the risks associated with collecting data and using a new technology with users under 18, a junior member of DE’s in-house legal team will participate in the discovery sprint during select portions of days 1,3,4 and 7. They will be looking for issues related to compliance with the Children’s Online Privacy Protection Act (COPPA), compliance with RFx/contract terms and conditions, and state adoption laws, policies, and procedures.
Day-to-Day Schedule
Day 1 | Monday
Morning
- Kick-off meeting
- Introductions
- Problem statement
- Map the challenge
Afternoon
Presentations and interviews with internal stakeholders, including:
- Strategy and Marketing (brand perception, product lines, market research)
- AWS Machine Learning case study
- Overview from Research team
- EdTech Adoption and Proposals team
- Social Impact team
- Kick-off meeting
Day 2 | Tuesday
Morning:
Continued internal stakeholder interviews and presentations, including:- Professional Development team
- Account Managers / Customer Success team
- Learning Analytics and UX Research data analysts
- Product teams for products not on sprint team
Afternoon
- Revisit map
- Set target
Day 3 | Wednesday
Morning
Expert interviews focusing on research-backed applications of AI in education, organized around:- Learning prediction
- Intelligent tutoring systems (ITS) / Cognitive Tutors
- Student behavior detection
- Automation
- Intelligent Textbooks
Afternoon
Continued interviews, centered on:- Large Language Models
- STEM-specific implementation
- Ethics
- Lack of uptake / market penetration
Day 4 | Thursday
Morning
- Lightning Demos from vendors
- Competitive Intelligence on competitors
- Implementation issues (Bing, Accenture)
Afternoon
“Brain dump” insights from expert interviews
Day 5 | Friday
Morning
Synthesis- Revisit map from Day 1 and revise based on new information and understandings
- Look for fit between user needs and AI capabilities
- Sketch ideas (individually)
- Solution critique (group)
- Storyboard
Afternoon
Build low-fidelity “fake” prototype to get feedback from users; most likely a “Wizard of Oz” prototype that mimics AI capabilities with a person behind the scenes.
Day 6 | Monday
A-Team
All day
Test lo-fi prototype with students;
Different regions, SES, and demographics representedB-Team
Morning
Demonstrate to teachers
Afternoon
Demonstrate to technologists, testing, curriculum staffDay 7 | Tuesday
A-Team
Morning
Receive feedback on prototype from parents / caregivers / guardiansB-Team
Morning
Interview and demonstrate to administrators (purchasing power)Afternoon
A-Team and B-Team meet to synthesize results from user tests and interviews only.
Day 8 | Wednesday
Morning
Synthesize all findings into outline
Afternoon
Heads down writing and slide drafting
First draft of slide deck and executive summary by EODDay 9 | Thursday
Morning
Socialize draft and get feedback
Afternoon
Apply feedbackDay 10 | Friday
Morning
Any last minute revisions based on feedback
Afternoon
Present deliverable to executive team
Wrap-up Dinner!
Leave a Reply