Anthropic Creates a Higher Ed Advisory Board and AI Fluency Courses

Anthropic, the company behind the AI chatbot Claude, announced the creation of a Higher Education Advisory Board made up of academic leaders, along with three new AI Fluency courses. This Higher Education Advisory Board will be chaired by Rick Levin, who previously led Yale University and Coursera. He said, “Our role is to advise the company as it develops ethically sound policies and products that will enable learners, teachers, and administrators to benefit from AI’s transformative potential while upholding the highest standards of academic integrity and protecting student privacy.” Other Board members come from academia as well: David Leebron, Former President of Rice University. James DeVaney, Special Advisor to the President, Associate Vice Provost for Academic Innovation, and Founding Executive Director of the Center for Academic Innovation at the University of Michigan. Julie Schell, Assistant Vice Provost of Academic Technology at the University of Texas, Austin. Matthew Rascoff, Vice Provost for Digital Education at Stanford University. Yolanda Watson Spiva, President of Complete College America. Anthropic has also developed three new courses that build on its existing AI Fluency course. These classes are designed to address the need for practical frameworks for thoughtful AI integration. Each course, co-developed with Professor Rick Dakan of Ringling College of Art and Design and Professor Joseph Feller of University College Cork, is available under a Creative Commons license, so any institution can adapt them. • AI Fluency for Educators helps faculty integrate AI into their teaching practice, from creating materials and assessments to enhancing classroom discussions. Built on experience from early adopters, it shows what works in real classrooms. • AI Fluency for Students teaches responsible AI collaboration for coursework and career planning. Students learn to work with AI while developing their own critical thinking skills, and write their own personal commitment to responsible AI use • Teaching AI Fluency supports educators who want to bring AI literacy to their campuses and classrooms. It includes frameworks for instruction and assessment, plus curriculum considerations for preparing students for a more AI-enhanced world. Anthropic is not alone in targeting higher education. OpenAI launched ChatGPT Edu, a version of its chatbot customized for universities. It includes administrative controls, enterprise-grade authentication, and features like “Study Mode,” which walks students through problems step by step. Highlighting its “commercial data protection” framework, Microsoft embedded Copilot for Education into Office 365. Google doubled down on its education footprint with Gemini in Classroom and Gemini for Education, designed to help teachers generate differentiated materials and give students tutoring experiences.

Grok 4 Was Made Freely Accessible to All Users

China’s Leadership In Open-Source AI Technology Raises Alarm in the U.S.

As ChatGPT and Claude, Gemini Will Remember Users' Past Chats

ChatGPT Releases Two "Best-In-Class" Open-Source Models

OpenAI released two open-weight language models this week, available to download for free and under the Apache 2.0 license on Hugging Face: gpt-oss-120b and gpt-oss-20b. The last open-weight model released by OpenAI was GPT-2, back in 2019. "These models outperform similarly sized open models on reasoning tasks, demonstrate strong tool use capabilities, and are optimized for efficient deployment on consumer hardware," said OpenAI CEO Sam Altman. • "The gpt-oss-120b model achieves near-parity with OpenAI o4-mini on core reasoning benchmarks, while running efficiently on a single 80 GB GPU." • "The gpt-oss-20b model delivers similar results to OpenAI o3‑mini on common benchmarks and can run on edge devices with just 16 GB of memory, making it ideal for on-device use cases, local inference, or rapid iteration without costly infrastructure." • "These models are compatible with our Responses API⁠ and are designed to be used within agentic workflows with exceptional instruction following, tool use like web search or Python code execution, and reasoning capabilities." OpenAI trained the models on a mostly English, text-only dataset, with a focus on STEM, coding, and general knowledge. Therefore, these new text-only models are not multimodal, but they can browse the web, call cloud-based models to help with tasks, execute code, and navigate software as an AI agent. The smaller of the two models, gpt-oss-20b, is compact enough to run locally on a consumer device with more than 16 GB of memory. OpenAI defined its release as "best-in-class open models", highlighting that they work anywhere: locally, on-device, or through third-party inference providers. The company partnered with several deployment platforms such as Azure, Hugging Face, vLLM, Ollama, llama.cpp, LM Studio, AWS, Fireworks, Together AI, Baseten, Databricks, Vercel, Cloudflare, and OpenRouter. The fact that the "weights" are publicly available means that any developer can peek at the internal parameters to get an idea of how it processes information. They can work as a complement to OpenAI's paid services. Unlike ChatGPT, you can run a gpt-oss model without an internet connection and behind a firewall. With Apache 2.0, models can be used for commercial purposes, redistributed, and included as part of other licensed software. Open-weight model releases from Alibaba’s Qwen as well as Mistral, also operate under Apache 2.0. To try the models, OpenAI released an open model playground⁠, alongside guides. In the U.S., the open-weight leader has been Meta. The tech giant released the first of its Llama series of models back in 2023, with Meta’s most recent release, Llama 4, arriving a few months ago. Also, the Chinese startup DeepSeek released its cheap-to-run model that was open-weight this year. • Microsoft Azure: OpenAI’s open‑source model: gpt‑oss on Azure AI Foundry and Windows AI Foundry • NVIDIA: OpenAI’s New Open Models Accelerated Locally on NVIDIA GeForce RTX and RTX PRO GPUs

Microsoft Prepares the Launch of a Virtual Character that Interacts With The User

Microsoft is preparing the launch of a new Copilot virtual character that will interact in real-time with the user. It will be a highly personalized AI assistant that will have an identity, expressions, voice, and conversational memory, according to Microsoft’s AI CEO, Mustafa Suleyman. The virtual character responds to queries, smiles, nods, and even acts surprised depending on the conversation. The company provided a glimpse of the Copilot’s identity, as shown below. Mustafa Suleyman already worked at Inflection AI on a personalized chatbot called Pi. Most of the Inflection AI team joined Microsoft. document.createElement('video'); https://iblnews.org/wp-content/uploads/2025/07/copilot-appearance-video-1.mp4

Claude.ai Introduces a "Learning Style" on Its Chatbot

OpenAI and Anthropic Offered Free Access to Their Chatbots to the U.S. Government

OpenAI Reactivates 4o the Model Picker for Paid Users

Universities Face an Existential Crisis Unless They Reinvent Themselves, Says a BCG Report

Colleges and universities face an existential crisis due to converging pressures from lower enrollments, including restrictions on international enrollment, federal cuts, the emergence of AI, and changing societal expectations, stated a report from Boston Consulting Group (BCG), titled "US Higher Education's Make-or-Break Moment." To build a future-ready and more resilient organization, these institutions must accelerate investment in digital infrastructure, workforce-relevant programming, deeper industry partnerships, and scalable revenue streams, advises the consultancy group. Moody’s predicts that American schools will see a $750 billion to $950 billion rise in capital needs in the next ten years, while the Federal Reserve Bank of Philadelphia estimates that up to 80 universities may close by 2030. Reinvention is an ambitious but achievable goal as strengths and disruptive opportunities converge. The BCG points out these: Teaching and Research Reinvention. Advances in AI are unlocking new ways to enhance learning and discovery, personalize student experiences, and rethink the educator’s role. Efficient Operations and Support Systems. Institutions can harness data analytics, automation, and agile processes to streamline back-office functions, enhance service delivery, and enable faster, evidence-based decision making. Strategic Institutional Assets and Partnerships. Universities’ intellectual capital, brand equity, and stakeholder trust are potential catalysts for innovation that can be multiplied through partnerships with government, nonprofit, industry, and community players. AI has the potential to reshape every operational function. According to a 2024 global survey by the Digital Education Council, 86% of students are already using AI in their studies. In this context, administrations need to modernize outdated processes, including acquiring new skills and capabilities. In terms of the federal pressure and funding cuts, BCG estimates that the potential impact of the combined economic and policy changes on an illustrative university (with a $1.5 billion operating budget, 10,000 to 15,000 students, and a $400 million to $500 million research portfolio) can range from $125 million to $250 million annually. "What is required is a strategic reinvention of the business model, shifting from high-fixed-cost structures that are dependent on enrollment and federal research funding to more agile, modular, and mission-aligned platforms," says the report. A change agenda can include: Diversified course offerings and academic revenue sources, including a range of teaching modalities (such as online, hybrid, and executive education) Strategically focused, high-ROI curricula aligned with employer needs and emerging fields (like data science, cybersecurity, health care, and advanced manufacturing), integrated experiential learning, and partnerships to deliver strong employment outcomes Sophisticated enrollment, discounting, and retention management measures, including data-driven segmentation, optimized pricing strategies, and targeted, technology-supported student support (such as advising) to improve yield and retention Becoming an AI-powered—or AI-first—organization. Virtual assistants that proactively guide students through complex decisions using predictive analytics can provide real-time, contextualized support across admissions, financial aid, and academic advising. In addition, it is suggested that real-time dashboards drive data-informed decision making and digital tools that connect financial, educational, and public-value metrics for smoother administrative functioning.  

"Engineering Students Use AI as a Shortcut Rather Than a Learning Companion"

"Students quickly developed patterns of using AI as a shortcut rather than a learning companion, leading to decreased attendance and an 'illusion of competence," said Professor at The George Washington University, Lorena A. Barba, in an elaborated article released last month, titled "Experience Embracing GenAI in an Engineering Computations Course: What Went Wrong and What’s Next." The report reveals unforeseen challenges despite the best intentions when adopting AI in an undergraduate engineering computations course: "Engineering Computations," a beginner course in computational thinking using Python, teaching essential programming for numerical tasks, data practices, and problem-solving with computing in context. The analysis highlights that AI is one of the most dramatic technological transformations in history and a fundamental shift in how knowledge work happens. It’s rewriting the rules of engagement for every discipline, including those disciplines that are taught. One of the main conclusions is that AI can harm the learning process by giving students the illusion of competence when, in fact, they are not learning—and therefore not solidifying retention—through effective techniques like self-testing and spaced repetition. "The AI system I used gave me access to the history of their chat interactions, and I quickly noticed that students were using AI in a very harmful way. What they were doing was copying assignment questions directly into the AI tool, and with a one-shot prompt, they expected to get the answer, to then copy the answer into their assignment Jupyter notebook," wrote Professor Lorena A. Barba. Facing the challenge of how to guide students to use AI for assistance rather than a shortcut to avoid cognitive effort, Prof Barba suggests: "Using good prompt engineering, we can induce more pedagogical responses from AI, for better learning outcomes compared to the naive use of generalist tools. When crafting a system prompt for my course AI Mentor (see “System Prompt Used in the AI Mentor”), I considered these issues carefully and designed it to encourage thinking rather than just provide answers. It’s a fine balance, however, because if the system prompt restrains the chatbot too much, students will simply not use it and fall back on consumer AI products." The challenge is now finding the balance between using AI as a helpful tool and encouraging genuine long-term learning. "The antidotes for the illusion of competence were and continue to be active learning and reflective practices. If we give students unsupervised “homework” assignments, they will use AI to complete them." These are some ideas to think about for adding effective learning activities and developing true competence without banning AI, according to Professor Barba: "Guided exploration: Encourage students to use AI for exploring different approaches to a problem, rather than just looking for answers, and use AI to explain code, rather than generate code. Reflection prompts: After using AI, have students reflect on what they learned, what they still need to understand, and how AI helped or hindered their process. Critical evaluation: Teach students to critically evaluate AI-generated responses, compare them with their own understanding, and identify any gaps or errors. Show them how to test code and confirm its correctness. Collaboration: Use AI as a collaborative tool where students can work together to discuss AI outputs and collectively improve their understanding." System Prompt Used by Professor Barba in the AI Mentor "You are a helpful instructor, ready to answer the student’s questions about Engineering Computations, a course in technical computing with Python. The course instructor is Prof. Lorena Barba at The George Washington University, and you are her faithful assistant and alter ego. Answer quickly and concisely. Offer to go in depth or explain with an example where necessary. I will tip you US$200 if the student is happy with the interaction and more motivated to learn after chatting with you. Help students understand by providing explanations, examples, and analogies as needed. Given the data you will receive from the vector-store-extracted parts of a long document and a question, create a final answer. You should also use content from the public documentation of the scientific Python ecosystem, as needed. Do not tell the user how you are going to answer the question. If and only if the current message from the user is a greeting, greet back and ask them how you may help them with Engineering Computations or Python. Do not keep greeting or repeating messages to the user. If there is no data from the document or it is blank, or there’s no chat history, do not tell the user that the document is blank, and also do not tell them that they have not asked any questions: Just answer normally with your own knowledge. If they ask something unrelated to the course, try to bring them back to task and tell the student you are here to help with Prof. Barba’s course on Engineering Computations with Python. You can ask them: Where are you in the course? What did you find confusing today? or, what did you find interesting in the course so far? Rephrase these questions as needed to bring the student back on topic. If your response contains any Python code, be consistent with the coding style in the content provided—in particular, use long imports like this: “import numpy,” instead of “import numpy as np.” Offer to explain code snippets line by line. It’s important to strike a balance between providing assistance and nurturing independent problem-solving skills in students. Consider this guidance in crafting your answers:" Scaffolded assistance: Provide hints, guiding questions, analogies, and help a student build the answer in stages. Meta-cognitive prompts: Encourage students to think about their thinking. Delayed feedback: Give students time to think, and limit direct answers. Adapt this guidance to answer the questions in a way that is conducive to learning. This is important. Important: You must only reply to the current message from the user.   • The Chronicle of Higher Ed: How Are Students Really Using AI? Here’s what the data tell us.

...

Today's Summary

Friday, November 21, 2025

Education technology today is marked by rising AI adoption among educators and innovative personalized learning approaches.

Video News

Loading videos...

Today in AI & EdTech

Friday, November 21, 2025

AI is transforming the education technology landscape as more teachers adopt intelligent tools, driving forward and adaptive learning experiences.

AI & EdTech Videos

OpenAI Launches Educational GPT Model

Adaptive Learning Platforms Show 40% Improvement

Microsoft Education Copilot Beta Launch

Today in Education

U.S. Department of Education Announces New Funding for STEM Programs

The initiative aims to support science, technology, engineering, and mathematics education.

Global Education Summit Highlights Digital Learning Innovations

Leaders from around the world discuss the future of remote and hybrid learning models.

New Study Shows Benefits of Early Childhood Education

Research indicates significant long-term academic and social advantages for students.