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Welcome Team Red! 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?” question here. “What if…?”
đź”´Â Red Team's Solution and Identified Problem:
The Red Team's solution addresses student disengagement, lack of opportunity, and authenticity in the educational system. They believe that maximizing student engagement is crucial and that understanding engagement metrics can help identify and resolve inequitable practices.
Desired Engagement with Tool:
I’ve engaged in my learning and collaborated with my peers/teacher.
We focused on a subject — on academics, on learning, on ourselves.
I’ve reflected on the experience and I’ve generated a subjective experience.
With Generative AI, my peers and I can now upload that data and chart an updated, iterated learning path to keep us moving forward.
I’ve taken ownership of my learning.
A tentative name is Subject[I’ve]
Alternative names: PivotPoint; Input:Orchestrate;
đź’» Product Overview:
The Red Team's product is a digital platform that utilizes Generative AI to analyze student engagement and performance data. It offers personalized learning paths based on individual student data, real-time feedback for educators, and a comprehensive view of student progress. The platform aims to transition from a time-based system to a competency-based education model, focusing on mastery of skills and content rather than seat time.
đź›—Â Elevator Pitch:
Our product is a transformative digital platform that leverages AI to enhance student engagement and personalize learning paths. Providing real-time feedback and insights empowers educators to create student-centered educational experiences and promote lifelong learning.
🏫 Who and What the Product Serves:
The product serves educators, students, and key stakeholders in the educational system. Educators can input and track data, receive real-time analytics, and access personalized learning path suggestions. Students benefit from personalized learning paths, immediate feedback, and a holistic understanding of their progress. The platform also supports the transition to competency-based education, enabling flexible and tailored learning experiences.
➡️ Potential Timeline:
The product development timeline includes building the minimum viable product (MVP) with essential features, measuring efficacy and effectiveness through short-term objectives, and allocating resources for technology, development, and team requirements. Piloting the product with select groups will lead to system-wide adoption.
🏆 Main Points and Purpose:
The purpose of the product is to maximize student engagement, promote authentic learning experiences, and address the limitations of the current time-based education system. It offers actionable insights, supports competency-based education (particularly with the XQ Learner Outcomes), and empowers educators to design personalized learning paths. By leveraging AI and real-time feedback, the product facilitates a shift towards student-centered education and fosters a lifelong love of learning.
Opportunity
Use this block to remind the audience of the one solution the Team selected to build out to solve a problem/challenge “INSIDE OF THE HIGH SCHOOL CLASSROOM or IN THE HIGH SCHOOL EDUCATIONAL SYSTEM”. This should be a clear and compelling summary of:
- What problem/challenge is the Team trying to solve? We are trying to optimize for engagement—maximize student engagement, believing students have a lack of opportunity and authenticity, which promotes it.
- What benefits does the solution offer? Understanding engagement metrics, which, if engagement is development over time, the trends in development can help us to identify and resolve inequitable practices.
- Why is this a priority? It’s about students having, maintaining, and advocating for their agency over their learning.
Notes
Our Solution: Orchestration—Students build an agenda through competencies; AI to support process and efficiency; School designed to support the collective agendas
- How do you scale, get the logistics, and track the implementation of the system? AI helps to mitigate
- Engaging with students with learning disabilities—students don’t necessarily have learning disabilities, and much as schools have teaching disabilities
- Make things look visible, make it multi-modal
- Need to consider the foundational, common knowledge that helps cohorts of students build relationships, sense of belonging and community.
- Takes feedback and turns it into actionable steps; is this the right ontology
Opportunity Outline: Generative AI Implementation for Feedback Analysis and Learning Orchestration
There are two primary systems that are restrictive factors of student learning: The Carnegie Unit and the metrics of student success, which are often tied exclusively to content-specific standards. This product aims to alleviate the reliance on such antiquated systems of what “success” is.
The Carnegie Unit system, which is essentially a time-based reference for measuring educational attainment, has been a foundational element of the U.S. education system for over a century. While it provided a standardized measure for academic progress, it also inadvertently promoted the idea that learning is a function of time. In this system, students advance based on the amount of time they spend in a classroom (seat time) rather than their mastery of skills and content.
The proposed Generative AI tool can help schools transition from this time-based system to a more competency-based education (CBE) model in the following ways:
- 🛣️ Personalized Learning Paths: The tool can analyze individual student engagement and performance data to create personalized learning paths. Instead of progressing through subjects based on age or grade, students can move forward based on their mastery of specific competencies.
- 🕰️ Real-time Feedback: Traditional models often rely on periodic testing to gauge student understanding. With the Generative AI tool, educators can receive real-time feedback on student engagement and understanding, allowing for immediate intervention and support.
- ⚖️ Holistic Understanding of Student Progress: By synthesizing subjective experiences into objective data, the tool can provide a more comprehensive view of student progress—individually and within groups. This includes academic achievements and soft skills, critical thinking, and other competencies that aren't always measured in traditional grading systems. Additionally, students can learn in dynamic cohorts, where a variation of learning modes, skill levels, and camaraderie drive collaboration among students.
- ↔️ Flexibility in Learning: Without the constraints of the Carnegie Unit system, students can spend more time on areas they find challenging and accelerate through areas they grasp quickly. This flexibility ensures that students truly understand a topic before moving on.
- 💪🏽 Empowering Educators: With actionable insights from the tool, educators can design curriculum and lessons that cater to the actual needs of their students rather than adhering strictly to grade-level standards. This can lead to more innovative and effective teaching methods.
- 🎓 Promotion of Lifelong Learning: By focusing on competency rather than seat time, students are encouraged to adopt a mindset of lifelong learning. They understand that learning is an ongoing process, not just something that happens within the confines of a classroom.
In essence, the Generative AI tool can help dismantle the rigid structures of the Carnegie Unit system by promoting a more fluid, student-centered approach to education. By focusing on actual competencies and providing real-time feedback, schools can ensure that students are genuinely prepared for future challenges rather than just progressing through a system based on time spent in a seat or on arbitrary content measurements that do not successfully approach the real-life skills required for collaboration, critical thinking, creativity, metacognition, or problem-solving.
Solution Benefits:
- Engagement Metrics: The tool will provide a comprehensive understanding of engagement metrics, offering a more holistic view of student experiences.
- Developmental Trends: By viewing engagement as a developmental trajectory over time, the tool can highlight trends that indicate areas of strength and areas needing intervention.
- Addressing Inequities: With insights into engagement and development, educators can identify and rectify inequitable practices, ensuring a more balanced and fair educational environment.
- Actionable Insights: Beyond solely the user-generated data inputs, the tool will synthesize information into actionable steps, allowing immediate and effective interventions.
Unlocking Competency-Based Education with Our Product
In order to evolve our antiquated learning systems beyond the traditional metrics of seat time restrictions, a more dynamic, student-centered approach needs to be embraced—enter Competency-Based education (CBE). The product isn’t just another tool; it’s a gateway to this transformative shift. Learning isn't about hours spent but skills mastered—our platform would seamlessly identify key skills within existing curricula, align them with specific competencies, and offer a foundational support system lacking current educator support tools.
But it's not just about personalization. Continuous feedback replaces traditional grading, offering students real-time insights into their progress. Every competency achieved is backed by evidence, ensuring genuine understanding and application. For educators, the transition to CBE can be daunting. That's why our platform must come equipped with training modules, guiding them through the nuances of this approach. Collaboration tools would further allow educators to share resources and strategies, fostering a community of continuous learning.
Piloting this shift is also easier with a tool as specific yet comprehensive as this. Schools can test the waters with select groups, refining their approach based on real data from our platform. As the benefits become clear, scaling to a system-wide adoption is just a few clicks away. In essence, our product isn't just a tool; it's a bridge to the future of education. A future where learning is flexible, personalized, and truly centered around the student. Welcome to the era of Competency-Based Education.
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.
- 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! |
Product Development
- Minimum Viable Product (MVP):
- Vision: The MVP will be a digital platform that uses Generative AI to analyze individual student engagement and performance data, creating personalized learning paths. It will allow educators, students, and other key stakeholders to input user-generated data to receive real-time feedback on student engagement and understand holistic progress.
- Features and Functionality:
- User Profiles: For educators and students to input and track data.
- Real-time Analytics Dashboard: Displaying student engagement metrics, areas of strength, and areas needing improvement.
- Personalized Learning Path Generator: Using AI to suggest learning paths based on student data, framing much of the language and output around competency-based education practices (using the XQ Learner Outcomes and SPF Navigator as a baseline).
- Feedback Mechanism: For students to provide subjective experiences and for educators to input observational data.
- Sketches/Mock-ups: TK TK TK
- Measurement of Efficacy and Effectiveness:
- Validation: Short-term benefits can be validated by tracking user engagement with the platform and the accuracy of the AI-generated learning paths. Long-term benefits can be validated by tracking student performance over time and correlating it with engagement metrics and personalized learning paths.
- Short-term Measurement Objectives:
- Resources:
- Core Technologies Needed:
- Generative AI algorithms for data analysis and learning path suggestions.
- Cloud-based storage for data.
- Web and mobile application development platforms. Lead with Mobile first platforms and vertical UX.
- Time to Develop: TK TK TK.
- Team and/or Skillsets Required:
- AI and Machine Learning Engineers
- Web and Mobile App Developers
- UI/UX Designers; Students for UX feedback
- Educational Consultants for content validation
- Data Analysts for metric tracking
- Large Expenditure Considerations: TK TK TK TK
SHORT TERM | ||
Number of educators actively using the platform | Active User Count | Increase by XX% in XX months |
Accuracy of AI-generated learning paths | Accuracy Rate | Achieve XX% accuracy in XX months |
Student engagement metrics improvement | Engagement Improvement Rate | Increase by XX% in XX months |
Feedback from educators on platform usability | Positive Feedback Rate | Achieve XX% positive feedback in XX months |
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?
- Are there data privacy concerns?
- Are there ethical considerations or biases that need to be raised?
- What are the worst-case scenarios if this solution is developed?
- Technical risks
- Are there any data challenges?
- Are there any engineering challenges?
- What bottlenecks could exist in development?
Ethical Considerations and Risks in Product Development
Responsible Tech
Who else needs to be at the table to validate the efficacy of the solution?
- Educators and Administrators: Their insights into the practical application of the tool in real-world classroom settings are invaluable.
- We cannot risk alienating educators and school administrators from the development of any tool.
- Students: As the primary users, their feedback on usability and effectiveness is crucial.
- We need to ensure that students understand this tool as an extension of their learning, not a replacement for their learning experiences.
- In addition, students need to understand the mechanics of these systems, so a user experience should not completely remove them from the complexities of how a system operates to the degree that they become wholly dependent to it.
- Parents: They can provide perspectives on how the tool affects their child's learning and overall well-being.
- Concerns about over-reliance on screen time for their children and larger concerns about data privacy, literacy on the subject, and the long-term impacts data has over its rapid, dynamic evolution into our daily lives.
- Data Privacy Experts: To ensure the tool adheres to all data protection regulations and best practices.
- Ethicists: To guide discussions on potential moral dilemmas and unintended consequences of reliance on AI tools.
Are there data privacy concerns?
- Student Data: Collecting and analyzing student data can raise concerns about confidentiality and misuse. Further, districts and schools must adhere to stringent policies and regulations about housing student records.
- Data Storage: Ensuring data is stored securely to prevent breaches or loss.
- Third-party Sharing: Ensuring no unauthorized sharing of data with third parties. It’s in our interests to promote open-source tools for transparency and community iteration.
Are there ethical considerations or biases that need to be raised?
- Algorithmic Bias: Ensuring the AI doesn't inadvertently favor or disadvantage any group of students.
- Accessibility: Ensuring the tool is equally effective for students of all backgrounds and abilities.
- We need to consider students with learning, cognitive, and physical disabilities, as well as our students from traumatic backgrounds and English language learners. If AI products can serve these students first, chances are they are good for all students.
- Over-reliance & Support: While the risk of educators becoming too dependent on the tool is of some lesser concern, the greater risk is dependence not met with consistent maintenance and support, potentially sidelining the efficacy and productivity of teams.
What are the worst-case scenarios if this solution is developed?
- Data Breach: Unauthorized access to sensitive student data. (highest concern)
- Misguided Decisions: If the tool provides inaccurate insights, it could lead to misguided educational strategies. (moderate concern)
- Exacerbating Inequalities: If not implemented thoughtfully, the tool could inadvertently widen educational disparities. (moderate concern)
Technical Risks
Are there any data challenges?
- Data Quality: Ensuring the data fed into the system is accurate, representative, and comprehensive enough to meet the needs of those who use it. (Example: Based on a unified set of competencies, includes general, well-tuned prompt examples, etc.)
- Data Volume: Managing vast amounts of data input and users without compromising system performance.
Are there any engineering challenges?
- Integration: Ensuring the tool integrates seamlessly with existing educational systems and platforms. It can’t simply be new facemask atop GPT 4 or future iterations, but rather a responsive, integrative tool.
- Scalability: Ensuring the tool can handle growth in terms of users and data volume, as well as future iterations of GPT or other LLM systems.
What bottlenecks could exist in development?
- Resource Limitations: Ensuring adequate resources (both human and technical) are available throughout the development process.
- Feedback Loops: Ensuring timely feedback from pilot tests to refine the tool effectively.
- Regulatory Hurdles: Navigating legal or regulatory challenges, especially data privacy, as laws and regulations are updated.
Landscape & Rollout Model
Use this block to share more about:
- 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?
1. Position
Current Market Trends and Capitalization:
- Personalized Learning: There's a growing trend towards tailoring education to individual student needs. Our product capitalizes on this by using AI to analyze students’ engagement and adapt learning pathways accordingly.
- Additionally, we aim to excel beyond the PL tracks to ensure that the collaborative, generative experiences between students is still a considerable component of their learning experiences.
- Competency-Based Education (CBE): As educational institutions move away from traditional time-based metrics, our product offers a solution that aligns with CBE principles, emphasizing mastery over seat time.
- Data-Driven Decision-Making: Schools and districts are increasingly relying on data to inform their strategies. Our tool provides actionable insights based on real-time student engagement and user-generated data.
Current Solutions Landscape:
- Existing Landscape: There are Learning Management Systems (LMS) that track student progress and digital tools that offer personalized learning pathways. However, few combine real-time engagement analysis with AI-driven adaptability.
- Product Differentiation: Our product stands out by tracking and interpreting engagement data to offer actionable insights. It's designed to integrate seamlessly with CBE practices, making it a unique offering in the market.
2. Total Addressable Market
Solution's Primary Audience:
- Students: As the primary beneficiaries, students form the core user base.
- Educators and Administrators: They will use the insights from the tool to adapt teaching strategies and curriculum planning.
Other Stakeholders:
- Parents: With a vested interest in their child's education, parents represent a significant secondary audience.
- Policymakers and Educational Institutions: As decision-makers, they can influence the broader adoption of the tool in educational systems.
3. Distribution Plan
Path to Adoption:
- Pilot Programs: Launch in select schools or districts, particularly XQ partnership schools, to gather initial feedback and demonstrate efficacy.
- Partnerships: Collaborating with educational institutions, policymakers, and ed-tech companies to promote broader adoption.
- Training Workshops: Offering training sessions for educators to understand and utilize the tool effectively; use these workshops also to gain valuable feedback for others.
First Adopters and Later Adopters:
- First: Interested schools or districts open to innovative solutions, tech-curious educators, and parents.
- Later: Larger school districts educational institutions, and policymakers once the product has demonstrated success and reliability.
Media Channels for Marketing:
- Educational Conferences and Seminars: A platform to introduce the product to a concentrated audience of educators and decision-makers.
- Ed-Tech Blogs and Magazines: Reaching out to educators and administrators looking for the latest tools and solutions.
- Social Media Campaigns: Targeted ads and informational content on platforms frequented by educators, parents, and students.
Appendix
- Acknowledgments
- Glossary of terms
- Academic papers
- Educational toolkits
- Code repositories (if applicable)
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! |
