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Generative AI in Edtech: Trends from the Pipeline
The document provides an overview of trends in generative AI tools for edtech based on research of over 280 AI startups. It finds the largest concentrations of tools are in study tools, language learning, and teacher assistants/co-pilots. It organizes the tools into categories and secondary categories, finding study tools make up 20% of tools. The document also provides market maps and analyses of the personalized learning/support and career/skill development categories, which received the most funding. It outlines key considerations for technology, data strategy, and pedagogy when developing generative AI for education.
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Generative AI in Edtech: Trends from the Pipeline
- 1. © 2023 ReachCapital. Confidential. All rights reserved. Generative AI in Edtech Trends From 280+ AI Tools September 2023 © 2023 Reach Capital. All rights reserved.
- 2. 2 Background & Summary ●So far in 2023, over 280 edtech tools leveraging Generative AI and Large Language Models have surfaced through our pipeline, research and news sources. ● Many are new entrants whose product development have been accelerated and enhanced by Generative AI tools. Incumbent market leaders are also building and buying GenAI tools and capabilities. ● There is an outsized concentration of products in several categories: study tools, language learning, and teacher assistants and “co-pilots.” ● Keeping on top of the latest developments in AI and LLMs is key to retaining competitive advantage. However, while technology is changing rapidly, fundamental business and pedagogical principles for edtech retain their importance.
- 3. 3 GenAI Edtech CategoriesOverview From over 280 edtech tools in our pipeline and research, we organized them into 6 primary categories and 15 secondary categories. Operational Efficiency and Management Personalized Learning and Support Career and Skill Development Content Creation and Enhancement Research and Information Access Academic Integrity Study Tools Language Learning Teacher Assistants & Co-pilots Career Guidance & Skill Development Grammar and Writing Educational Content Creation Games & Gamification AI-Powered Research Virtual Tutors AI Cheating Detection Education Management Social & Emotional Learning Mental Health Workforce Empowerment Data Analytics and Insights
- 4. 4 GenAI Primary Categories- Overview & Funding Distribution of 285 companies from our pipeline and research, and known funding for each category. ● Personalized Learning, along with Career and Skill Development, represent two-thirds of the GenAI pipeline ● Among these companies, 36 successfully secured funding in the past two years, raising over $350 million between the two categories. ● The remaining categories make the remaining $96 million of disclosed funding over the past two years.
- 5. 5 GenAI Secondary CategoriesDistribution Distribution of companies within each secondary category ● Study tools were the most popular, comprising 20% of all GenAI companies. ● AI-powered study tools offer a wide range of features targeted to students, including quiz, flashcard, and personalized assessment generation, content summarization, and notetaking functions. # of Companies
- 6. Category Deep Dive &Market Map
- 7. 7 Tools and platformsthat provide personalized learning experiences and instructional support for teachers and students. These tools enable tailoring education and support based on the unique needs, preferences, and learning styles of individuals, promoting personalized and targeted learning outcomes. Subcategories ● Study Tools ● Virtual Tutors ● Teacher Assistants & Co-pilots ● Social and Emotional Learning ● Mental Health Key Takeaways and Observations Personalized Learning & Support Common Applications ● Study and Exam Prep Tools: Study materials, quizzes, flashcards and step-by-step explainers that provide tailored resources and personalized recommendations. ● Learning Support and Tutoring: AI chatbots and tutors, real-time support, simplified explanations, and personal guidance to support academic and mental well-being. ● Teacher Workflow Optimization: Automation of administrative tasks such as grading, lesson planning, progress monitoring for improved efficiency and time-savings. Differentiating Factors ● Depth of personalization and adaptability to individual learning needs and the ability of AI tools to understand to user queries can have high impact on the user experience. ● Comprehensiveness and quality of study materials, as well as the clarity, depth and personalization of feedback and explanations. ● Integration and compatibility with existing learning management systems (LMS), content and major educational platforms.
- 8. 8 Emerging Funded Incumbent PersonalizedLearning & Support Study Tools Virtual Tutors Teacher Assistants & Co-pilots Mental Health & SEL Companies and their categorization on this map are illustrative, not exhaustive or definitive. Some companies span multiple categories.
- 9. 9 Common Applications ● PersonalCareer Guidance: Personalized guidance to individuals, helping them evaluate career paths and opportunities. ● Upskilling and Workforce Productivity: Personalized career learning, upskilling opportunities, productivity tools to empower individuals in the changing job landscape. ● Language, Writing and Communication: Conversational AI agents, writing assistants, interactive lessons to enhance language acquisition, writing and communication skills. Key Takeaways and Observations Differentiating Factors ● Ability to precisely infer skills from job requirements and individual work experiences ● Accuracy and relevancy of career recommendations, training modules, and skill development paths. ● Workflow integration and compatibility with existing workforce training and HR talent management tools and processes Career & Skill Development Tools and platforms for career development, skill improvement, and upskilling opportunities, along with tools for language acquisition and communication improvement. These solutions empower individuals in their career journey and promote lifelong learning. Subcategories ● Career Guidance ● Skill Development ● Workforce Learning ● Language Learning ● Writing
- 10. 10 Career Guidance & SKillDevelopment Workforce Learning Language Learning Writing Emerging Funded Incumbent Career and Skill Development Companies and their categorization on this map are illustrative, not exhaustive or definitive. Some companies span multiple categories.
- 11. 11 Key Takeaways andObservations Common Applications ● Educational Materials Creation: Platforms that streamline the creation of content and courses, including tools that transform texts into interactive lessons and engaging presentations. ● Games & Gamification: Dynamic and gamified learning content, adapting content based on learners’ actions and tailored to user preferences and learning goals. ● Immersive Learning: Immersive learning experiences powering AR/VR environments, adapting content for more dynamic and interactive learning experiences. Content Creation & Enhancement Tools and platforms that enable the rapid creation of engaging and high-quality interactive educational content and courses, leveraging animations, videos, quizzes, and augmented and virtual reality, to enhance the learning experience through immersivity and interactivity. Subcategories ● Educational Content Creation ● Games & Gamification Differentiating Factors ● Unique and immersive content that goes beyond mere duplication of existing materials in different media formats ● Effectiveness and engagement of learning experience, incorporating collaborative and social learning experiences ● Alignment with established academic curriculum standards and learning objectives ● Accessibility to learners with different abilities, learning challenges and cultural backgrounds
- 12. 12 Educational Content Creation Games and Gamification EmergingFunded Incumbent Content Creation and Enhancement Companies and their categorization on this map are illustrative, not exhaustive or definitive. Some companies span multiple categories.
- 13. 13 Common Applications ● AdvancedSearch and Information Retrieval: Search engines and tools that efficiently retrieve relevant information from vast databases of research papers and scientific literature ● Automated Summarization and Insights: Tools that can summarize and extract key information from research papers, providing quick insights and takeaways. ● Personalized Research Assistance: Chatbots or personal assistants that aid researchers in answering questions about research and finding related resources. Facilitate information access for educational and research purposes. These tools leverage AI to enhance search capabilities, surface relevant information, and navigate research papers or journals more efficiently and effectively. This can greatly expedite the process from research to application and development. Key Takeaways and Observations Research & Information Access Differentiating Factors ● Sophistication and accuracy of NLP capabilities to understand complex queries and retrieve relevant and reliable information from high quality sources. ● Access to proprietary (non-public) research and databases ● Reliability of methods to validate and verify sources ● Quality and accuracy of AI-generated summaries and key insights
- 14. 14 Tools that addressacademic integrity and assessment in educational settings. It includes AI cheating detection tools to detect plagiarism, identify AI-generated content, and track instances of cheating in student submissions. These tools aim to maintain academic integrity, uphold standards of honesty and originality, and ensure fair assessment practices. Key Takeaways and Observations Academic Integrity Differentiating Factors ● Accuracy and effectiveness in detecting a range of sophisticated and emerging AI language models and the content generated by them. Debate continues over how well AI text detectors work. ● Reporting and analysis features that help educators understand patterns of plagiarism or cheating behavior. ● Presence of a robust “Human in the Loop” that acts as a safeguard against false positives: The advantage of this system lies in its ability to offer a secondary layer of review, which can be leveraged to improve the algorithm's performance. Common Applications ● AI Detection: Recognizing AI-generated text by keeping up with leading AI content generation tools and LLMs to ensure academic integrity and originality. ● Plagiarism Detection: Software that identifies potential instances of plagiarism in student submissions.
- 15. 15 Key Takeaways andObservations Common Applications ● Education Management and Administration: Streamlining administrative tasks, providing consolidated solutions for education management, and optimizing resource allocation. ● Data Management and Analytics: Unifying data collection and insights across different tools and platforms, and surfacing real-time insights for analysis and reporting. ● Decision and Support Optimization: Facilitating data-driven decision making and enhancing operational efficiency in educational settings. Differentiating Factors ● Interoperability and integration capabilities to access data across different existing educational tools and processes. ● Accuracy and reliability in collecting, processing, and presenting data to ensure informed decision-making and insights. ● Cost-benefit ratio, taking into account the potential savings in time, resources, and improved educational outcomes. Operational Efficiency & Management Tools and platforms that streamline educational operations and management processes. It includes tools that assist in managing educational institutions, automating operational tasks, and informing data-driven decisions. These tools aim to improve efficiency, optimize resource allocation, and enhance decision-making processes in educational settings. Subcategories ● Education Management ● Data Analytics and Insights
- 16. 16 Research & Information Academic Integrity DataAnalytics & Insights Emerging Funded Incumbent Other Categories Companies and their categorization on this map are illustrative, not exhaustive or definitive. Some companies span multiple categories.
- 17. 17 ➢ Is theLLM implementation API-based or open source? ➢ If using an open-source model, how do you ensure it is appropriately fine-tuned to your specific use-case? ➢ If using APIs, how do you mitigate risks related to vendor reliability? ➢ What are your data sources and how much of your data is proprietary? ➢ How is data stored, processed, and secured? ➢ Do you have a formal data governance policy? ➢ LLMs provide the groundwork for a variety of applications by leveraging enormous data sets. ➢ The use of LLMs each come with its own set of implications, considerations, and risks, including quality of output and resource requirements. AI Maintenance & Evolution Key Considerations & Questions — Technology Large Language Models (LLM) Data Strategy ➢ Refers to the processes and strategies the company has in place for maintaining and improving its AI tools, which includes the availability of annotated data, methods to retrain the model, and ongoing performance monitoring systems. ➢ What is your plan for ongoing AI model maintenance and improvement? ➢ What processes do you have in place for fine-tuning your AI model? ➢ It is crucial to understand where the data is sourced from (public or proprietary) and how it's being used. ➢ Ensure best practices are being followed in data governance, including data acquisition, data privacy, and security.
- 18. 18 Integration Capabilities AI FailureResistance Long-Term Scalability ➢ Understanding how companies handle AI errors and what their remediation strategy will look like will be critical. ➢ This could include UI designs to handle AI errors gracefully, backup plans for AI failures, or robust methods for users to report and resolve issues. ➢ The architecture of the AI system should be extensively evaluated to ensure that it can handle a growing user base and increased data loads. ➢ Companies should have plans to scale infrastructure, ensure data storage capacity, and maintain performance. ➢ The ease and robustness of product integrations, with LMS, SIS, or school specific software, can affect the user experience and therefore the overall success of the product. ➢ What are your strategies for handling AI mistakes or failures? ➢ What systems are in place to collect and integrate user feedback for continual model improvement? ➢ What is your error rate? ➢ How do you ensure the performance of your application as user base expands? ➢ Is the current technology infrastructure in place prepared to support growth? ➢ What are the cost implications? ➢ What is your strategy for ensuring seamless integration and user experience? ➢ How do you manage updates and changes in the systems that your AI tool integrates with? Key Considerations & Questions — Technology
- 19. 19 ● Effective pedagogyreigns supreme. The best edtech is informed by educational research, and AI that can further bring to life and scale proven pedagogical practices will be a key differentiating factor. ● Straddling multiple workflows (such as lesson planning, grading and feedback for teachers) without compromising on quality is key. This puts a premium on design that is empathetic to existing user workflows and pain points. ● Key metrics retain their significance. Ease of use, high customer retention and NPS scores, consistent user engagement, and healthy unit economics all remain crucial. ○ However, Generative AI may also recalibrate best-in-class benchmarks via lowering CAC via sales and marketing automation, reduced content costs, etc. Other Key Considerations & Questions
- 20. Building AI for Educationand the Future of Work? Check out our AI Catalyst www.reachcapital.com/ai-learning-catalyst