Summary of https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-faculty-survey

The Digital Education Council's Global AI Faculty Survey 2025 explores faculty perspectives on AI in higher education. The survey, gathering insights from 1,681 faculty members across 28 countries, investigates AI usage, its impact on teaching and learning, and institutional support for AI integration.

Key findings reveal that a majority of faculty have used AI in teaching, mainly for creating materials, but many have concerns about student over-reliance and evaluation skills. Furthermore, faculty express a need for clearer guidelines, improved AI literacy resources, and training from their institutions.

The report also highlights the need for redesigning student assessments to address AI's impact. The survey data is intended to inform higher education leaders in their AI integration efforts and complements the DEC's Global AI Student Survey.

Here are the five most important takeaways:

• Faculty have largely adopted AI in teaching, but use it sparingly. 61% of faculty report they have used AI in teaching. However, a significant majority of these faculty members indicate they use AI sparingly.
• Many faculty express concerns regarding students' AI literacy and potential over-reliance on AI. 83% of faculty are concerned about students' ability to critically evaluate AI output, and 82% worry that students may become too reliant on AI.
• Most faculty feel that institutions need to provide more AI guidance. 80% of faculty feel that their institution's AI guidelines are not comprehensive. A similar percentage of faculty feel there is a lack of clarity on how AI can be applied in teaching within their institutions.
• A significant number of faculty are calling for changes to student assessment methods. 54% of faculty believe that current student evaluation methods require significant changes. Half of faculty members believe that current assignments need to be redesigned to be more AI resistant.
• The majority of faculty are positive about using AI in teaching in the future. 86% of faculty see themselves using AI in their teaching practices in the future. Two-thirds of faculty agree that incorporating AI into teaching is necessary to prepare students for future job markets.

Summary of https://storage.googleapis.com/coscientist_paper/ai_coscientist.pdf

Introduces an AI co-scientist system designed to assist researchers in accelerating scientific discovery, particularly in biomedicine. The system employs a multi-agent architecture, using large language models to generate novel research hypotheses and experimental protocols based on user-defined research goals.

The AI co-scientist leverages web search and other tools to refine its proposals and provides reasoning for its recommendations. It is intended to collaborate with scientists, augmenting their hypothesis generation rather than replacing them.

The system's effectiveness is validated through expert evaluations and wet-lab experiments in drug repurposing, target discovery, and antimicrobial resistance. Furthermore, the co-scientist architecture is model agnostic and is likely to benefit from further advancements in frontier and reasoning LLMs. The paper also addresses safety and ethical considerations associated with such an AI system.

The AI co-scientist is a multi-agent system designed to assist scientists in making novel discoveries, generating hypotheses, and planning experiments, with a focus on biomedicine. Here are five key takeaways about the AI co-scientist:

• Multi-Agent Architecture: The AI co-scientist utilizes a multi-agent system built on Gemini 2.0, featuring specialized agents (Generation, Reflection, Ranking, Evolution, Proximity, and Meta-review) that work together to generate, debate, and evolve research hypotheses. The Supervisor agent orchestrates these agents, assigning them tasks and managing the flow of information. This architecture facilitates a "generate, debate, evolve" approach, mirroring the scientific method.
• Iterative Improvement: The system employs a tournament framework where different research proposals are evaluated and ranked, enabling iterative improvements. The Ranking agent uses an Elo-based tournament to assess and prioritize hypotheses through pairwise comparisons and simulated scientific debates. The Evolution agent refines top-ranked hypotheses by synthesizing ideas, using analogies, and simplifying concepts. The Meta-review agent synthesizes insights from all reviews to optimize the performance of other agents.
• Integration of Tools and Data: The AI co-scientist leverages various tools, including web search, domain-specific databases, and AI models like AlphaFold, to generate and refine hypotheses. It can also index and search private repositories of publications specified by scientists. The system is designed to align with scientist-provided research goals, preferences, and constraints, ensuring that the generated outputs are relevant and plausible.
• Validation through Experimentation: The AI co-scientist's capabilities have been validated in three biomedical areas: drug repurposing, novel target discovery, and explaining mechanisms of bacterial evolution and antimicrobial resistance. In drug repurposing, the system proposed candidates for acute myeloid leukemia (AML) that showed tumor inhibition in vitro. For novel target discovery, it suggested new epigenetic targets for liver fibrosis, validated by anti-fibrotic activity in human hepatic organoids. In explaining bacterial evolution, the AI co-scientist independently recapitulated unpublished experimental results regarding a novel gene transfer mechanism.
• Expert-in-the-Loop Interaction: Scientists can interact with the AI co-scientist through a natural language interface to specify research goals, incorporate constraints, provide feedback, and suggest new directions. The system can incorporate reviews from expert scientists to guide ranking and system improvements. The AI co-scientist can also be directed to follow up on specific research directions and prioritize the synthesis of relevant research.

Summary of https://cdn.openai.com/global-affairs/openai-edu-ai-ready-workforce.pdf

OpenAI's report examines the prevalence of ChatGPT use among college students in the United States and its implications for the future workforce. It highlights that students are actively using AI tools for learning and skill development, even outpacing formal educational integration.

The study identifies disparities in AI adoption across different states, which could lead to future economic gaps. The report advocates for increased AI literacy, wider access to AI tools, and the development of clear institutional policies regarding AI use in education.

It also emphasizes the importance of aligning educational practices with the growing demand from employers for AI-ready workers. The document uses data from ChatGPT usage and surveys of college students to support its findings and recommendations.

Here are 5 key takeaways from the source:

• State-by-state differences in student AI adoption could create gaps in workforce productivity and economic development.
  • The source indicates that employers are increasingly looking for candidates with AI skills. Because of this, states with low rates of AI adoption risk falling behind.
  • States like Utah and New York are proactively incorporating AI into higher education. For example, Salt Lake Community College is integrating AI experience into industry pipelines, and the University of Utah launched a $100 million AI research initiative.
  • In New York, the State University of New York (SUNY) system will include AI education in its general education requirements starting in 2026.
• Many students are self-teaching AI skills due to a lack of formal AI education in their institutions, which creates disparities in AI access and knowledge.
  • Many college and university students are teaching themselves and their friends about AI without waiting for their institutions to provide formal AI education or clear policies about the technology’s use. The rapid adoption by students across the country who haven’t received formalized instruction in how and when to use the technology creates disparities in AI access and knowledge.
  • The education ecosystem is in an important moment of exploration and learning.
• To build an AI-ready workforce, states should focus on driving access to AI tools, demystifying AI through education, and developing clear policies around AI use in education.
  • The source suggests that AI literacy is essential for students’ future success. However, while three in four higher education students want AI training, only one in four universities and colleges provide it.
  • The source suggests that teaching AI effectively requires practical examples that show students how AI can support their learning rather than replace it.
  • A nationwide AI education strategy—rooted in local communities and supported by American companies—will help equip students and the workforce with AI skills. Academic institutions, professors, and teachers must also lay out clear guidance around AI use - across classwork, homework, and assessments.