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From AI Tutor to AI Student - by Dr Philippa Hardman
- Teaching Others is a pedagogical approach where students teach concepts to others as a way of learning those concepts themselves
- But my focus has been on a particularly powerful pedagogical approach that has proven especially challenging to scale: Teaching Others.
- it lies in scaling high-impact pedagogical approaches that have traditionally been difficult or impossible to implement broadly.
- Perhaps the most well-known and most celebrated approach to teaching and learning is the vision of 1:1 Socratic coaching that guides students through complex problem-solving, one-on-one tutoring that adapts to individual learning paths, hands-on guided projects with immediate expert feedback.
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Saturday, January 11, 2025
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Where Open Education Meets Generative AI: OELMs – improving learning
- I am absolutely ready to predict that the large publishers will begin creating bundles of proprietary supplemental materials designed specifically for use with proprietary language models.
- we should take the initiative now to ensure that instructors who want to use LLMs as course materials have access to high quality, openly licensed options from the start.
- Those options should include both the models themselves and the additional resources necessary to use them easily and effectively.
- ensure that generative AI tools can move us forward on affordability, access, and equity instead of backward
- Open Educational Language Models (OELMs) bring together a collection of openly licensed components that allow an openly licensed language model to be used easily and effectively in support of teaching and learning
- But because the model weights are open, we have the opportunity to revise and remix them
- Because the model weights are open, we can change the way learners and teachers interact with them in order to increase access, affordability, and equity. Because the model weights are open, we have significantly greater agency
- An OELM includes a comprehensive collection of pre-written prompts
- For teachers, these activities might include lesson planning, designing an active learning exercise for use in class, differentiating instruction, revising or remixing OER, and drafting feedback on student work.
- what about students? students should have access to the prompts as well...
- When a teacher or learner submits a prompt to the model, before the prompt is sent to the model, relevant information is searched for in the collection of OER and added to the prompt.
- The model then uses the information it has retrieved from the OER as the basis for its response to the user, augmenting its general knowledge about the topic before generating a response.
- specially designed collection of open content that can be used to steer the model’s behavior
- This can be embedded in the system prompt (a prompt which the user doesn’t see but which steers model behavior in the background) or used for fine-tuning.
- In the OELM context, fine-tuning is the process by which a model can be made to behave more pedagogically
- Each of these four components – the model weights, content for fine-tuning, content for RAG, and pre-written prompts – can be openly licensed, providing teachers, learners, and others with permission to engage in the 5R activities.
- Retain, Reuse, Revise, Remix, and Redistribute
- Think of the model weights as the core textbook and the other components as the supplemental materials necessary for widespread adoption.
- And just like with traditional OER, the ability to copy, edit, and share prompts and other OELM components means they can be localized in order to best meet the needs of individual learners,
- The foundational R in the 5Rs framework is Retain
- Then you can take that copy you downloaded and revise, remix, reuse, and redistribute it to meet your needs and the needs of others around you.
- small models are a key to this strategy over the medium to long-term.
- Advances in running models locally are important because people without reliable access to the internet are currently unable to take advantage of generative AI in support of teaching and learning.
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Addressing the Real Challenges of Microcredentials
" the real challenges institutions face when trying to expand their microcredential offerings"
- the real challenges institutions face when trying to expand their microcredential offerings
- While these kinds of programs have long served adult learners looking to update their job skills or switch careers, research shows students fresh out of high school are flocking to them in greater and greater numbers. Learners ages 18 to 20 completed more certificates at higher ed institutions than any other age group during the 2022–23 academic year, according to an April 2024 report from the National Student Clearinghouse Research Center. Nearly 154,000 young learners earned certificates that year—an 11 percent increase over the previous year—among the 670,665 certificate earners across all ages.
- Many colleges and universities remain unsure of how to effectively launch and grow new microcredentials.
- US average of 64 microcredentials per institution reported in a recent UPCEA survey.
- The slow growth of microcredentials is largely due to the challenges colleges and universities face in implementing and expanding them, especially when attempting to do so in a centralized (or at least coordinated) manner.
- Finding reliable platforms to facilitate enrollment and payment for non-matriculated students (i.e., those not currently enrolled, for non-American readers). These systems should ideally track enrollments, direct payments to specific units, and handle functions such as wait-listing and refunds.
- Understanding the ecosystem and market for these tools, and how different segments intersect.
- Providing access to a learning management system (LMS) for non-matriculated students who don’t have regular university IDs
- Developing a more lightweight social ID for non-matriculated students, and determining what systems and supports
- Devising a business model and marketing strategy for microcredentials, which are typically priced far lower than degrees.
- Identifying and creating market-attractive microcredentials in an agile manner, often requiring new pedagogical approaches and course design methods that involve teams rather than individual instructors.
- Collaborating with the corporate sector to integrate real-world expertise into microcredentials and foster recognition of these credentials, particularly in the absence of widespread standards.
- these challenges, and potential solutions, are rarely addressed in the flood of publications on microcredentials
- Many sources on microcredentials focus heavily on defining what they are and fitting them into one of the larger frameworks that have been developed, such as the New Zealand Quality and Credentials Framework, the Australian National Micro-credential Framework, and the European Approach to Micro-credentials for Lifelong Learning and Employability, to name a few.
- I understand the need for definitions and frameworks, as they are important tools for addressing issues like shared language, quality assurance, and recognition by other educational institutions and employers.
- Frameworks are certainly useful and will be essential for developing standards for recognizing and transferring microcredentials. This work, for instance, is critical for standards bodies. But for those of us working on the ground, it’s time to move beyond comparing and perfecting frameworks. We need to focus on developing, supporting, and growing actual microcredential offerings.
- the primary model they use with higher education institutions is to offer microcredentials alongside a degree for currently enrolled students.
- You see this throughout the survey report but especially in the emphasis on offering microcredentials for credit.
- need to focus on explaining and exploring the challenges outlined at the beginning of this post, as well as other complex, hands-on issues colleges and universities face when rolling out microcredentials.
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National framework for microcredentials – Colleges and Institutes Canada
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How to win with generative engine optimization while keeping SEO top-tier
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Why agents are the next frontier of generative AI | McKinsey
- “agentic” systems refer to digital systems that can independently interact in a dynamic world.
- Gen AI agents eventually could act as skilled virtual coworkers, working with humans in a seamless and natural manner.
- a human user could direct a gen AI–enabled agent system to accomplish a complex workflow. A multiagent system could then interpret and organize this workflow into actionable tasks, assign work to specialized agents, execute these refined tasks using a digital ecosystem of tools, and collaborate with other agents and humans to iteratively improve the quality of its actions
- Google, Microsoft, OpenAI, and others have invested in software libraries and frameworks to support agentic functionality.
- shifting from being knowledge-based to becoming more action-based.
- The value that agents can unlock comes from their potential to automate a long tail of complex use cases characterized by highly variable inputs and outputs
- Agents can manage multiplicity
- Agent systems can be directed with natural language.
- Because agentic systems use natural language as a form of instruction, even complex workflows can be encoded more quickly and easily.
- done by nontechnical employees
- Foundation models can learn how to interface with tools, whether through natural language or other interfaces.
- control mechanisms are essential to balance autonomy and risk
- create a learning flywheel for ongoing improvement
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Saturday, August 3, 2024
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Análise de Dados para Workforce Management | Coursera
- Análise de Dados para Workforce Management Specialization
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