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Welcome Team Blue! This is the home for your final report. Once complete, please erase this copy block and the Timer. 60 mins! GO!
The big idea “What if?” ODYSSEY
Embark on ODYSSEY: Your Lifelong Learning Companion, built on an open architecture framework, where every stride in knowledge unveils a narrative, with a personal coach and a map to navigate the realm of endless possibilities, crafting a portrait of yourself ur unique learning journey, beyond the rudimentary measure of just grades, GPA’s or credentials. A queryable portrait, using cutting edge natural language computer— that can tell the whole story, as you forge your life long learning path!
ODYSSEY. Imagine a realm where the age-old tradition of teaching and learning is revolutionized, captured through a lens that paints the Ultimate Student Learning Portrait. Here, each student is the master of their knowledge, embarking on a journey crafted uniquely for them. What if there existed a tool, not just any tool, but a companion that evolves with every step you take on your learning journey? Welcome to a reality where education meets innovation, catalyzed by artificial intelligence.
Now envision a world where this sophisticated AI becomes your personal guidance counselor and coach, a companion that’s attuned to your every aspiration, strength, and even the spots that challenge you the most. It walks with you, mirroring your pace, illuminating the way, and unveiling your true potential. It's not merely about grades, credits, or badges; it's about painting a picture of your learning journey that's as vibrant and unique as you are.
This isn't just a transcript, it's about YOUR STORY! The narrative nuance, a story of your life long learning record, unfolds with every new experience you embrace, reflecting not just what you know, but who you are and what you aspire to be. This tool is your companion, a bridge between you and your educators, painting a vivid picture beyond mere numbers and letters. It showcases your essence to teachers, peers, and even to the gates of higher education, opening doors to opportunities tailored just for you.
Welcome to a new era, an era where learning is not just personalized but personified. Where your experiences, passions, and dreams are the colors that paint your unique Learning Portrait. Where AI is not just a tool, but a companion on a journey to self-discovery and beyond. This is not just education; this is your life, your story.
The Ultimate Learning Portrait — where you own what you know, and discover all that you can be.
Please use this page and its sections to summarize one Team's solution to a problem/challenge. This can be short paragraphs, bullets, sketches, etc. that provide a high-level overview of the product, idea, project, or concept. The purpose of this final report is to quickly and concisely communicate the main points to a reader.
Please take your time to craft a clear and compelling summary.
Opportunity
Odyssey AI open-architecture transcript Hypothesis: A framework to store and compute examples of learning that are beyond the scope of a transcript that has 3 benefits:
- Students feel and are better represented as human beings
- Employers and colleges can be more sophisticated about how they hire/admit
- Schools and educators have more data on how students achieve positive outcomes to update and optimize their curriculum
BACKGROUND: Large language models, powered by advanced machine learning algorithms, have revolutionized our ability to understand, generate, and interact with text in a nuanced and context-aware manner. These models, trained on vast amounts of data, can comprehend complex patterns in language, making them capable of processing and interpreting natural language entries in a way that was previously unattainable. This capability opens up new possibilities for educational records, moving beyond traditional, rigid transcript formats to a more dynamic and comprehensive representation of a student’s learning journey. By analyzing natural language entries, these models can extract meaningful insights about a student’s skills, interests, and progress, providing a richer and more holistic view of their educational experience. This not only allows for a more authentic representation of students but also enables employers and colleges to make more informed decisions. Furthermore, the data derived from this approach can offer valuable feedback to schools and educators, helping them to continually refine and enhance their curriculum. Thus, large language models serve as the technological backbone of this innovative approach to educational records.
#1 voted Solution From Vince Trost: Diploma Innovation “A NATURAL LANGUAGE WHOLE PERSON EVALUATION”: computing OVER natural language (using AI and natural language) to create a queryable LEARNER record that can be an array and collection of Human Testimonials/language-based/ Learning Experience diagnostic that translates beyond just Grades, and Credentials/Badges— “a Transcript Story”, that speaks to the whole person’s sense of flourishing (academic and beyond)
- What are the inputs? Could be localized design principles, student frameworks, course credits/ academic skills, badges,
- An incentive structure that incentivizes the Teacher, Coach/Advisor, and student, to make this “Portrait” a living breathing
- This is a “COMPREHENSIVE LEARNER RECORD”— to informally, and iteratively, allow the learner to OWN the PORTRAIT of the knowledge.
- Why is this a priority? The field needs a tangible vision and instantiation to work towards.
- Transcripts are ONLY “a part of us”, how do we create a transcript to be a LONGER tail, evolving portrait.
- Could have a Student HUD, where the student can Self asses, in an applied manner, and see WHERE their blind spots in their Transcript is based on where their Interests may be.
- Schools + Systems (primary buyer); Employers + Universities (secondary buyer)
- Could use a tool like nomic to visualize and cluster various XQ qualities and identify gaps Imagine a realm where the chapters of a student's learning journey are woven into a living narrative, transcending the traditional bounds of transcripts and grades. Welcome to the era of Odyssey , a groundbreaking framework emboldened by the prowess of advanced language models. Here, every stroke of curiosity, every quest for knowledge, is captured and celebrated in a rich tapestry of learning — The Query Language-based Student Profile.
At its core, the Odyssey harbors a triad of empowering outcomes:
- Humanity Restored: Students are no longer mere aggregates of grades and credits. They are recognized and revered as individuals, with their diverse learning experiences meticulously chronicled in a "Transcript Story" that mirrors their academic and personal flourishing.
- Informed Opportunities: Employers and universities delve into these enriched narratives, unearthing a deeper understanding of each learner, and paving the way for more enlightened admission and hiring decisions.
- Curricular Renaissance: Schools and educators, armed with a treasure trove of insights on student accomplishments and aspirations, are inspired to evolve, optimizing their curriculum to foster a nurturing environment for every learner.
The magic unfolds as Odyssey employs large language models to breathe life into educational records. These AI companions transcend the rigid structures of conventional transcripts, orchestrating a "Natural Language Whole Person Evaluation." Here, every project, every testimony, every badge earned, is a note in a symphony of personal growth, harmonized into a Queryable Learner Record.
Let's delve into the canvas where your Ultimate Learning Portrait comes to life:
- A Rich Palette of Inputs: Localized design principles, academic skills, course credits, badges, and heartfelt testimonials, all come together to sketch a vivid portrayal of a student's learning voyage.
- Living Incentives: A dynamic ecosystem where teachers, coaches, advisors, and students are bound in a symbiotic endeavor, nurturing the living, breathing entity that is the Learning Portrait.
- Owning Your Masterpiece: This Comprehensive Learner Record is a realm where learners are the maestros of their narrative, cherishing every milestone, every challenge overcome.
- A Vision Materialized: In a landscape thirsting for tangible progress, this initiative stands as a beacon of what the future of education could, and should, morph into.
- Evolving Portraits: The narrative transcends a static snapshot, morphing into a long-tail, evolving portrait that resonates with the essence of a student's journey.
- The Student HUD: A reflection pool where students self-assess, discerning the contours of their knowledge landscape, unveiling blind spots, and kindling the flame of self-improvement.
- Synergy Unleashed: Schools and Systems, the primary artisans, along with Employers and Universities, the connoisseurs, come together in a marketplace of enriched learning narratives.
- Visualization Mastery: Tools like Nomic are the lens through which the myriad facets of a learner’s profile are beheld, revealing a vista of qualities and potential waiting to be unlocked.
The Odyssey isn’t merely a framework; it’s the dawn of an educational renaissance. It’s a quest where each student, armed with the power of AI, embarks on a voyage of self-discovery, carving out their unique space in the annals of academia and life.
This is not just a transformation; this is a revolution waiting to unfurl. Odyssey beckons a future where the narrative of education is as boundless and brilliant as the minds it seeks to nurture.
Pilot with WIth a High school, as an alternate college admissions platform. Start in one geography.
- Quality of input and output
- Not a complete replacement
- Have each teacher speak to student’s abilities in whatever category is relevant
- Credit for non-traditional aspects of learning
- Incentives for kids to note when they do something and for people to input quick feedback
- build something in to make it easy to input information
- opt-in product
- Example: RI HS and colleges pilot innovation - alternative admissions process
- Extra credit for things outside of the classroom
- Level playing field for college recommendations
- Assumptions for the future - every classroom will have some AI tutor
- student psychological profiles - critical thinking
- learner owns learner record
- Model bias
Product
Use this block to share more about the product opportunity and development.
- Minimum viable product (MVP):
- Share your vision for what needs to be built in order to validate the solution.
- Discuss how specific features or functionality will address users’ needs or pain points.
- Share basic sketches/mock-ups to give a sense of how it looks and works.
Text interface where an admissions counselor or employer [or any entity that is interested in a students qualifications, abilities, etc.] can enter requirements and it will return a list of student candidates who possess the competencies and experiences desired, based on supporting data and evidence, delivered anonymously to mitigate bias associated with conspicuous diversities. (Students can also access this in a limited fashion, see below)
Core assumption(s):
• Queries are driven by an external reference point (school admission, job, internship); it’s not for looking at or comparing students directly in isolation. e.g. a student can put in admission criteria or job requirements to see what is highlighted about them. others can only query in relation to an opportunity.
• AI integrated into existing classroom and educator activities, tools, and processes.
• Students have the ability to directly to add their own data set (self-reported), distinct from what is objectively captured in real-time
- Measurement of efficacy and effectiveness
- How will you validate the short and long-term benefits from the MVP?
- Fill out optional short-term measurement objectives that demonstrate how you’ll get early signal.
- Resources
- Discuss the team, technology and other inputs required to make this happen. This could touch on:
- Core technologies needed
- Time to develop
- Team and/or skillsets required
- Large expenditure considerations or estimated budget
SHORT TERM | ||
Activity associated to Output | Output Metric (objective) | Output Target (unit or %) |
Type here! | Type here! | Type here! |
- The diploma is the portrait of a graduate (XQ Learner Goals, durable skills, etc.)
- Discrete academic skills are based on proficiency on key learning outcomes instead of courses/credits
- Students demonstrate proficiency through seminars (inside or outside of school), AI tutors, internships
- AI tools help guide students on what academic skills they need to achieve their post-high school goals
- AI tools show schools what experiences need to be provided for students to gain the necessary skills
- Tools exist to capture the durable skills proficiency levels from a variety of sources (teachers, mentors, work supervisors, coaches, etc.) to provide a multiple-point assessment of the student
- As students progress in their skill attainment, AI tools constantly assess their ability to achieve their goals and likewise, if a student changes his/her mind, the tools adapt and make new recommendations
Ethical Considerations & Risks
Use this block to share more about what we should address before launching this solution, both ethically and morally. Responsible technology begins with acknowledging technology is not inherently neutral. We need to deliberately consider all the potential impacts, good and bad, direct and indirect, that a solution that involves technology can create.
- Responsible tech
- Who else needs to be at the table to validate the efficacy of the solution?
- Students & their parents
- Teachers
- School principals
- Are there data privacy concerns?
- Yes, we have to think about who owns the data (e.g private companies? non-profit org? etc.) and who has access to it.
- Are there ethical considerations or biases that need to be raised?
- Thoughts and learnings not represented in traditional metrics might not be tracked and represented
- Not all students have access to tech/teacher support that can allow them to contribute their data to the system
- Lack of data might result in a lack of representation
- What are the worst-case scenarios if this solution is developed?
- Admission officer’s over-reliance on the model to present students’ background that is biased by how the data was collected and processed
- Technical risks
- Are there any data challenges?
- FERPA and the current system of tracking every little thing will be a huge Trust hurdle.
- yes, data collection is a challenge. What data are we tracking and when are we collecting that? How do we decide what data is important to track and could there be biases? (Ex: art vs. engineering vs humanities etc) How do we decide what’s holistic?
- Where and how do we store the data so it’s secured? How much data are we planning to store per student and who is going to fund the infrastructure necessary to achieve this?
- Extracting useful information from the query: Retrieval bias due to query inputs
- Difficulty in compiling all retrieved information into insights that are useful for the admission officer/teacher
- Compression/summary of the information could be very biased
- What about forged data from the students? How can we prevent the data collection process from being exploited?
- Are there any engineering challenges?
- Ensure privacy, managing permissions & data ownership can be a technological challenge
- we need incentives to deploy and adopt those NEW approaches
- we need the INPUTS to measure (which map to the curricular experience).
- Assumptions/Pre-requisites [Bob and Scott]
- The existing structural constraints (Carnegie units, funding models, master schedules) are realigned to students’ needs and outcomes.
- Data privacy and end-user control are paramount for trust and adoption.
- Teacher's role is changed to facilitator and learning coach who can better capture learner experiences.
What bottlenecks could exist in development?
With the current structure of SYSTEM, we will have very limited inputs out of the existing Structure inputs..i.e. grades, internships,
in order for this to work we NEED Richer Curricular experiences
Landscape & Rollout Model
Use this block to share more about:
1) User Testing:
Friendly school (educator and student), friendly college admissions office, and friendly employer
Goal: Sanity check of idea with a mock-up of the end product with dummy data.
Success if: College and Employer end users would find it useful for admissions or employment to get high-value candidates they were not otherwise.
Success if: Student end users would find it useful in better see where their skill set ladders up to - how they can better nurture their interests and gifts to align with opportunities
Success if: School educator end user would find it useful in that they would believe it would increase student engagement, attendance, [note challenge here to incentivize teacher - it would be great if this somehow teacher workload]
2) Pilot:
Friendly school (educator and student), friendly college admissions office, and friendly employer
Must be same geographical area so interested in the same student body.
Success if: The Platform is able to capture significantly more feedback on students than previously - both academic and outside experiences - from traditional and non-traditional sources of feedback
Success if: Students find this additional information about themselves motivating to engage with new activities to build skillsets. The most compelling end user is Students - this product should help illuminate future growth paths by showing them where they are already strong and how it correlates to the real world.
Success if: Admissions rate at target post-secondary institutions indicates students admitted based on excellence outside of traditional measurements
Success if: Employer recruitment rate at target employers indicates students offered jobs based on excellence outside of traditional measurements
- Position
- What are the current market trends, and are there trends the solution is capitalizing on?
- What does the current solutions landscape look like, and where does this product fit in? Similarly, why is this product different?
- Total addressable market
- Who does the solution serve and how big is that population?
- Who are the other potential stakeholders who will interact with or be affected by the solution and how big is that population?
- Distribution Plan
- What is the path to adoption to ensure this gets into the education system? Into the hands of the user? Are there specific stakeholders that need to be involved?
- Who are the first adopters (students, teachers, administrators, parents, policymakers, educational institutions, etc.), and who comes later on?
- What media channels exist that would be well-suited to market the product?
Appendix
- Acknowledgments
- Glossary of terms
- Academic papers
- Educational toolkits
- Code repositories (if applicable)
- Past Data/Black Box Cautionary Tales:
OPTIONAL EXERCISE: Building with Measurement and Efficacy: Guiding the Implementation of AI in Evidence-based Educational Solutions
Aiming to strike a balance between fostering efficacy and ensuring agility in the development cycle of potential software innovations, this exercise invites participants to foresee and strategize the integration of efficacy-boosting tactics, all without curbing innovation or overtaxing resources.
Measuring Your Innovation's Effectiveness
Keep the learning objectives of your innovation forefront during this exercise. Embracing responsible design requires pinpointing and evaluating the elements of your innovation set for testing. Below is a template illustrating how to align your objectives with measures of effectiveness, steering towards responsible design and continuous refinement.
Provocations for reflection: What metrics will inform the effectiveness? How will you collect and interpret data to infer effectiveness?
Exercise:
1. NEAR TERM Efficacy Objective Identification:
What specific outputs do you plan to track and measure in the short term to evaluate the progress and success of your innovation during the immediate period (within 3-6 months of deployment)?
For all intents and purposes of this exercise, we define an "output" as the immediate results of an innovation’s activities, tasks, and actions. Output metrics are specific, measurable, and tangible.
Propose meaningful metrics that you can reasonably achieve and are ready to be held accountable for.
- Purpose: To identify the key efficacy objectives, propose functions to achieve them, and determine the frequency of these functions."
SHORT TERM | ||
Activity associated to Output | Output Metric (objective) | Output Target (unit or %) |
Type here! | Type here! | Type here! |
2. MEDIUM-LONG TERM Efficacy Objective Identification:
What medium- to long-term outcomes do you anticipate your innovation delivering for learners beyond in the medium term (within 9-18 months of deployment)?
How do you plan to measure and evaluate these intended outcomes to track the success and impact of your innovation over time? This exercise defines "outcome" as the specific and measurable changes, effects, or benefits that will result from the implementation of your innovation. Within this theme, focus on how your innovation will build toward your GOALS! This will be specific to the project, but for example, you could inquire about how to measure the tool's impact on career path selection or describe how it will help learners develop self-efficacy in relation to educational and career goals.
Propose meaningful metrics that you can reasonably achieve and are ready to be held accountable for.
- Purpose: To identify the key efficacy objectives, propose functions to achieve them, and determine the frequency of these functions.
MEDIUM TERM | ||
Activity associated to Output | Output Metric (objective) | Output Target (unit or %) |
Type here! | Type here! | Type here! |
