AI in Education (Sunday, November 16, 2025)

Date: Sunday, November 16, 2025

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Key Industry Updates

From Microsoft

• The 2025 “AI in Education Report” shows that 86% of education organizations worldwide are now using generative AI tools. (Microsoft)

• Yet, there’s a major gap in training: less than half of U.S. students and only about half of educators say they’ve had meaningful AI training. (Microsoft)

• Key take-aways from the report:

  1. AI adoption is accelerating in education. (Microsoft)

  2. AI is positioned not as a replacement for teaching, but as a creative and collaborative partner. (Microsoft)

  3. AI fluency is becoming a workforce imperative — students need to develop AI-related skills to be ready for future careers. (Microsoft CDN)

  4. While the potential is high, challenges remain: responsible usage, readiness, equity. (Microsoft)

From OpenAI

• In their “Building an AI‑Ready Workforce” they report that among U.S. college-aged students (18-24): more than 1/3 use ChatGPT, and for those users, over a quarter of their messages are related to learning/tutoring/school work. (OpenAI)

• The report emphasizes that many students are self-teaching AI tools and usage, often without formal instruction or institutional support. (OpenAI)

From Google

• Google’s recent blog “Our latest commitments in AI and learning” announced $30 million in new funding for learning-projects, plus new research studies on the impact of AI in education globally. (blog.google)

• They also announced partnerships (e.g., with Estonia’s “AI Leap” initiative) and upcoming tools like conversational AI for learning videos (e.g., in YouTube) and wider access to their model Gemini for students and educators. (blog.google)

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What This Means for Teaching & Learning

Here’s how these developments affect different grade levels:

Elementary (Grades K-5)

• Opportunity: AI tools can help personalize learning — e.g., summarizing ideas, generating differentiated prompts, supporting language or math remediation. The Microsoft report shows students are already using AI for tasks like brainstorming (37 %) and summarizing (33 %). (Microsoft)

• Caution: Younger students may use AI as a shortcut rather than a learning partner. Without proper scaffolding, there's risk of reduced engagement or over-reliance.

• Action for educators:

  • Introduce age-appropriate AI literacy: what AI is, what it can/ can’t do.

  • Use AI tools with guided tasks (e.g., “Use the AI to help you brainstorm ideas, then we will revise together”).

  • Set clear usage guidelines in classroom: what is acceptable, what is not.

Middle School (Grades 6-8)

• Opportunity: Students at this level are ready for more sophisticated self-directed tasks. AI can support differentiated instruction — generating multiple versions of problems, scaffolding writing, offering feedback suggestions.

• Caution: The teacher-training gap is real: even though many schools adopt AI, fewer than half of educators feel highly prepared. Students may face inconsistency in how AI is integrated. (Microsoft)

• Action for educators:

  • Provide mini-lessons on how to use AI effectively (not as answer machine, but as thinking partner).

  • Integrate metacognitive reflection: after using AI, students reflect on what the AI did and what they still did themselves.

  • Collaborate with other teachers/IT staff to develop consistent policies across classes for AI usage.

High School (Grades 9-12)

• Opportunity: High school students are already using AI tools for school work and preparing for college/careers. The adoption data from OpenAI shows usage is significant at this age. (OpenAI) AI can help with complex tasks: research, coding, writing, problem-solving.

• Caution: Academic integrity concerns rise. The Microsoft report flags issues such as plagiarism and responsible use. (Microsoft) Also, students may skip the learning process if they rely purely on AI suggestions.

• Action for educators:

  • Revise assignments to embed AI use/expectation: e.g., include component where students show how they used AI, what they modified, reflections on AI output.

  • Offer AI fluency modules: e.g., what prompts work best, how to evaluate AI output, how to use with critical thinking.

  • Partner with counseling / career-readiness teams: help students understand AI competence is becoming part of workforce readiness. The Microsoft report mentions that institutions are under pressure to prepare students accordingly. (Microsoft CDN)

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Grant Ideas & Resources

Grant Ideas

1. AI Literacy Across Grades Initiative: A proposal to fund professional development + student modules that teach AI literacy (what it is, ethics, prompting, evaluation) from elementary through high school.

2. AI-Enhanced Differentiation Pilot: Fund use of AI tools to support differentiated learning in a middle school setting (e.g., generating scaffolded content, alternative problems) and evaluate impact.

3. Student AI Authorship Portfolio Project: High school students use AI tools to support research/writing, but also reflect on and annotate the AI’s output, documenting their revisions and thought process — grant funds tools, training, student exhibition.

4. AI Career Readiness Program: Given the mention that AI fluency is a workforce imperative, a grant could support a program in high school exploring AI in careers, certifications, partnerships with local tech firms, and mentoring.

Resources

• Microsoft’s “AI in Education Report” + the “AI Toolkit” for planning. (Microsoft)

• OpenAI’s “Building an AI-Ready Workforce” white paper. (OpenAI)

• Google’s “Advance Education Using Google AI” site, which includes tools, guidelines, and resources. (Google for Education)

• For policy & ethics: the U.S. Dept. of Education’s report “Artificial Intelligence and the Future of Teaching and Learning.” (U.S. Department of Education)

What Is Vibe Coding — And Why It’s Changing the Way We Build with AI

If you’ve ever sat in front of a coding window thinking, “I know what I want, I just don’t want to write all those lines of code,” you’re not alone. Welcome to vibe coding—a new creative frontier of programming where ideas, not syntax, lead the way.

The Origin of “Vibe Coding”

The term vibe coding was introduced by Andrej Karpathy, former Tesla and OpenAI engineer, who described it as:

“There’s a new kind of coding I call ‘vibe coding,’ where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.”

Rather than meticulously writing each line, developers describe what they want in natural language, and an AI model—such as ChatGPT, Claude, or GitHub Copilot—translates those ideas into functional code.

The Core Idea

Vibe coding represents a shift from manual programming to AI-guided creation. The human developer provides goals and feedback, while the AI handles much of the coding process. You’re not abandoning code entirely—you’re orchestrating it from a higher level.

A typical workflow looks like this:

1. Describe the goal – “Create a quiz app that records student scores.”

2. AI generates the code – HTML, JavaScript, CSS, or even backend logic appears in seconds.

3. Run and test – Evaluate what works and what doesn’t.

4. Iterate by prompting – Ask the AI to refine or expand features.

5. Deploy and reflect – Finalize the prototype and consider what you’ve learned.

The key is that the developer’s role shifts from coder to designer of ideas—someone who directs and evaluates rather than types and compiles.

Why It Matters

Benefit	Description
Speed	Rapid prototyping dramatically shortens the time from idea to product.
Accessibility	People with limited coding experience can now create digital tools.
Creativity	By removing technical barriers, it frees attention for problem-solving and innovation.
Educational Value	Students can focus on logic, design, and reflection rather than syntax errors.

The Challenges

Like any paradigm shift, vibe coding comes with limitations. Because AI generates most of the code, understanding how it works can be challenging. Debugging, maintaining, and scaling may become complex if developers rely too heavily on AI outputs.

Other common concerns include:

• Hidden bugs or security vulnerabilities that go unnoticed.

• Overreliance on AI without verifying logic or ethical implications.

• Lack of deep understanding of the systems being built.

For these reasons, vibe coding is most useful for prototyping, creative projects, and educational applications, rather than large-scale software engineering.

Implications for Educators

In education—particularly teacher preparation and instructional technology—vibe coding offers meaningful opportunities:

• Pre-service teachers can design classroom tools, simulations, or digital stories through AI guidance rather than coding from scratch.

• Instructor-led workshops can emphasize prompt design and iterative testing instead of traditional syntax lessons.

• Research applications include examining how AI-assisted creation affects creativity, digital confidence, and reflective thinking.

Classroom Example

Suppose education students are tasked with creating interactive art vocabulary games. Instead of teaching them HTML and JavaScript from the ground up, you might have them prompt an AI:

“Create a drag-and-drop matching game for art terms using HTML and CSS.”

The AI provides a starting version. Students then test it, critique it, and adjust their prompts or logic. Through this process, they practice inquiry, design thinking, and reflection—all without the frustration of debugging syntax.

Conclusion

Vibe coding isn’t about replacing programmers—it’s about expanding access to creation. For educators, it redefines what it means to teach technology: not memorizing commands, but thinking critically, creatively, and collaboratively with AI.

The next time you have an idea for a classroom tool, lesson activity, or interactive learning experience, try describing it to an AI assistant. You might be surprised at how quickly your concept comes to life—and how much you learn in the process.

ChatGPT Prompts for University-Educators

 https://chatgpt.com/use-cases/university-educators

Artificial Intelligence: A Comprehensive Briefing

I. Introduction to Artificial Intelligence (AI)

Artificial Intelligence (AI) is a rapidly evolving field of computer science focused on creating systems capable of performing tasks that typically require human intelligence. While the term "AI" can have multiple interpretations, the core concept revolves around the development of computational systems that can "perceive their environment and take actions that maximize their chances of achieving defined goals."

The broad cultural awareness of AI can be traced back to seminal works like the 1968 film "2001: A Space Odyssey," featuring "HAL," a "Heuristically-programmed ALgorithmic" computer. HAL's human-like behaviors such as reasoning, talking, and acting, illustrate AI's potential to assist humans while also highlighting "unanticipated risks—especially since AI reasons in different ways and with different limitations than people do."

Defining "Intelligence" in AI

Various definitions of "intelligence" within AI highlight its computational nature:

• John McCarthy: Defines intelligence as "the computational part of the ability to achieve goals in the world."
• Marvin Minsky: Describes it as "the ability to solve hard problems."
• Leading AI Textbooks: Define AI as "the study of agents that perceive their environment and take actions that maximize their chances of achieving defined goals."

These definitions emphasize problem-solving and goal attainment as key measures of a machine's "intelligence."

AI Models

To assess AI meaningfully, it is crucial to understand specific models and their development. The term "model" in AI often refers to a "mathematical model," distinct from usage in phrases like "model school" or "instructional model," as it is not an exemplar created by experts.

II. Core Goals and Capabilities of AI

AI systems are designed to achieve various intelligent behaviors, encompassing a wide range of human-like cognitive functions.

A. Key Goals of AI

• Reasoning and Problem-Solving: AI aims to solve problems through logical deduction and heuristic approaches. This includes problem-solving, puzzle-solving, game-playing, and deduction.
• Knowledge Representation: Systems are built to represent and store information about the world, enabling them to understand and process data effectively.
• Planning and Decision-Making: AI involves automated planning and decision-making, often utilizing decision theory and game theory to determine optimal actions, even when involving multiple interacting agents.
• Learning: A fundamental aspect of AI is its ability to learn from data. This includes supervised learning (learning from labeled data), unsupervised learning (identifying patterns in unlabeled data), and reinforcement learning (learning through trial and error with rewards).
• Natural Language Processing (NLP): AI systems can understand, interpret, and generate human language, enabling conversational interactions and text analysis.
• Perception: Machine perception allows AI to use sensory input (e.g., cameras, microphones) to deduce aspects of the world, including computer vision, speech recognition, and object recognition.
• Social Intelligence: Some AI systems are designed to recognize and simulate emotions and facilitate more natural human-computer interaction, a field known as affective computing.
• General Intelligence: The ultimate, aspirational goal for some AI researchers is Artificial General Intelligence (AGI), which would enable AI to perform any intellectual task that a human can.

B. Noteworthy AI Techniques

AI research employs diverse techniques to achieve its goals:

• Search and Optimization: AI can solve problems by "intelligently searching through many possible solutions."
• State Space Search: Explores a "tree of possible states" to find a goal.
• Local Search (Mathematical Optimization): Refines an initial guess incrementally, often using "gradient descent" to train neural networks. Evolutionary computation and swarm intelligence algorithms (e.g., particle swarm optimization, ant colony optimization) are also used.
• Logic: Formal logic, including propositional and predicate logic, is used for reasoning and knowledge representation, enabling deductive reasoning and problem-solving through proof trees.
• Probabilistic Methods for Uncertain Reasoning: Techniques like "Bayesian networks" are used for reasoning, learning, planning, and perception in uncertain environments.
• Classifiers and Statistical Learning Methods: These include decision trees, K-nearest neighbor, and support vector machines for categorization and pattern recognition.
• Artificial Neural Networks and Deep Learning: These are models inspired by biological neural networks, with "deep learning" referring to networks with multiple layers, revolutionizing areas like speech recognition and image classification.
• Generative Pre-trained Transformers (GPT): These are "large language models (LLMs) that generate text based on the semantic relationships between words in sentences." GPT models are pre-trained on vast text corpora and can "generate human-like text by repeatedly predicting the next token." However, they are "prone to generating falsehoods called 'hallucinations'," a problem that has been worsening for reasoning systems. Reinforcement learning from human feedback (RLHF) and quality data are used to reduce these falsehoods.

III. Applications of AI

AI is being applied across numerous sectors, demonstrating its versatility and impact.

• Health and Medicine: AI assists in areas like skin cancer detection, cough sound diagnosis, personalized cardiovascular medicine, and drug discovery (e.g., finding new antibiotics, accelerating Parkinson's drug design).
• Games: AI has achieved superhuman performance in various games, including chess (Deep Blue), Go (AlphaGo, MuZero), poker, and StarCraft II. It can also learn to play open-world video games by observation.
• Mathematics: LLMs like GPT-4 are increasingly used, though they can "produce wrong answers in the form of hallucinations." Techniques such as supervised fine-tuning and training models to produce correct reasoning steps are being developed to improve their accuracy.
• Finance: AI is used in financial analysis and operations.
• Military: AI has been deployed in military operations in various conflicts.
• Generative AI (GenAI): A significant subfield that uses generative models to produce "text, images, videos, or other forms of data." These models learn patterns from training data to create new content based on natural language prompts.

IV. Ethical Considerations and Risks of AI

The widespread adoption of AI brings significant ethical challenges and potential risks that require careful consideration and regulation.

A. Risks and Harm

• Privacy and Copyright: AI systems raise concerns about data privacy and the intellectual property rights of content used for training. Data can be used to train AI models without explicit consent.
• Dominance by Tech Giants: The development and deployment of advanced AI are largely concentrated among major tech companies, leading to concerns about monopolization and control over the innovation ecosystem.
• Power Needs and Environmental Impacts: AI data centers consume vast amounts of energy, comparable to small countries. This surge in power demand leads to exploration of energy sources like nuclear power to meet AI's needs, as "nuclear power plants are the most efficient, cheap and stable power for AI."
• Misinformation: AI technologies can be used to generate and disseminate "computational propaganda and misinformation," making humans more likely to believe AI-generated disinformation.
• Algorithmic Bias and Fairness: AI systems can exhibit bias, leading to unfair or harmful outcomes, especially if the training data reflects existing societal biases. This is a significant concern for regulators, who argue that if "the problem has no solution, the tools should not be used."
• Lack of Transparency (Black Box AI): Many advanced AI systems operate as "black boxes," where the reasoning behind their decisions is opaque. This poses a challenge, particularly in critical sectors like medicine, where "doctors, for example, are expected to clearly and completely explain to their colleagues the reasoning behind any decision they make."
• Bad Actors and Weaponized AI: The dual-use nature of AI means it can be exploited by malicious actors, including for surveillance systems and potentially autonomous weapons.
• Technological Unemployment: There are concerns that AI will automate jobs, leading to significant displacement of human workers. Some industries, such as game art, have already seen jobs "decimated by growing AI use."
• Existential Risk: Some prominent figures in AI, such as Geoffrey Hinton, warn of the potential for AI to pose an "existential threat" to humanity, even suggesting a "50–50 chance' that AI outsmarts humanity." However, others argue that these fears are misplaced and that AI is not an imminent risk.

B. Ethical Frameworks and Regulation

• Human in the Loop AI: A key recommendation in AI development is to maintain "Human in the Loop AI," emphasizing human oversight and intervention in AI processes.
• Right to Explanation: People harmed by algorithmic decisions should have a right to an explanation. While recognized in early drafts of the EU's GDPR, industry experts note it's an "unsolved problem with no solution in sight" for many AI systems.
• International Cooperation and Treaties: There is growing international consensus on the need for safe and responsible AI development, with initiatives like the Bletchley Declaration and the Council of Europe's global treaty on AI.
• Open Source AI: The debate continues on whether to make powerful AI models open source. Proponents argue it fosters innovation and transparency, while critics raise safety concerns.

V. AI in Higher Education

While not explicitly detailed across all sources, the "Academics: Artificial Intelligence: Guidelines for Using AI Tools in Writing and Research" from Walden University and "Using AI in the Higher Education Classroom" from the University of North Texas indicate that higher education institutions are actively developing resources and guidelines for integrating AI. This implies a recognition of AI's growing role in academic settings and the need to address its implications for teaching, learning, writing, and research.

Universities are providing "Student Resources" and "AI Resources Hubs" to guide appropriate AI tool usage, acknowledging both the opportunities and challenges AI presents in an educational context. The context suggests that these guidelines address academic integrity, research methodologies, and ethical considerations for students and faculty.

What Is NotebookLM? (Podcast AI tool!)

NotebookLM is an AI-powered research and note-taking tool developed by Google Labs, designed to help users synthesize and understand complex information by grounding its responses in user-provided documents. Launched in 2023 as "Project Tailwind," NotebookLM has evolved into a versatile assistant for students, researchers, writers, and professionals.(Wikipedia)

What Is NotebookLM?

NotebookLM utilizes Google's Gemini AI to process and analyze various document formats, including PDFs, Google Docs, Google Slides, and web pages. By uploading up to 50 sources into a notebook, users can interact with the AI to:(Wikipedia, The Guardian)

• Generate summaries and key takeaways.

• Ask questions about the content.

• Create study guides, timelines, and FAQs.

• Produce AI-generated audio overviews in a podcast-like format.(Reddit, blog.google, Time, Tom's Guide)

Unlike general-purpose AI chatbots, NotebookLM's responses are grounded solely in the uploaded materials, enhancing accuracy and relevance. (blog.google)

Key Features

• Audio Overviews: NotebookLM can convert documents into engaging audio discussions between AI-generated hosts, allowing users to listen to summaries and explanations on the go. (Time)

• Interactive Mode: Users can "join" the audio conversations to ask questions or steer the discussion, making the experience more dynamic and personalized. (blog.google)

• Mobile Accessibility: Available on both Android and iOS, the NotebookLM app enables users to access features like Audio Overviews, real-time Q&A, and content sharing directly from their mobile devices. (blog.google)

• Creative Assistance: Beyond research, NotebookLM aids in creative endeavors by helping track story elements, suggesting edits, and generating ideas or text. (Tom's Guide)

NotebookLM Plus and Enterprise

For users requiring advanced capabilities, Google offers NotebookLM Plus, a premium subscription that includes:(blog.google)

• Increased limits on notebooks and sources.

• Enhanced customization options for responses.

• Shared team notebooks with usage analytics.

• Additional privacy and security features.(blog.google)

NotebookLM Enterprise caters to organizational needs, providing enterprise-grade security and compliance, making it suitable for businesses, schools, and universities. (Google Cloud)

Real-World Applications

NotebookLM has demonstrated effectiveness in various fields:

• Education: Serving as a collaborative tutor, NotebookLM assists students in understanding complex subjects through guided interactions and personalized study materials. (arXiv)

• Healthcare: In clinical settings, NotebookLM has been used for tasks like lung cancer staging, showcasing its ability to process medical guidelines and provide accurate assessments. (arXiv)

• Content Creation: Writers and journalists utilize NotebookLM to organize research, extract key information, and even generate podcast scripts, streamlining the content development process. (Tom's Guide)

Getting Started

To explore NotebookLM:(blog.google)

1. Visit notebooklm.google and sign in with your Google account.

2. Create a new notebook and upload your documents.

3. Interact with the AI through summaries, questions, or by generating audio overviews.

4. Download the mobile app for on-the-go access.(Wikipedia, DataCamp, Tom's Guide)

Whether you're delving into academic research, managing complex projects, or seeking creative inspiration, NotebookLM offers a powerful platform to enhance your productivity and understanding.(Tom's Guide)

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• Tom's Guide
• Time
• The Verge

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Student Information Systems in Indiana

Student information systems (SIS) are essential software applications that help educational institutions manage student data, including demographics, enrollment, attendance, grades, and more. In Indiana, several SIS platforms are used across K-12 schools and higher education institutions. Here's a comprehensive list of student information systems utilized in Indiana:

K-12 Student Information Systems

Indiana DOE Ed-Fi Certified Systems

The Indiana Department of Education (IDOE) has implemented the Data Exchange project using Ed-Fi technology standards to streamline data transfer between schools and the state. These Ed-Fi certified systems can automatically transfer data to the IDOE rather than requiring manual submissions:

1. Harmony School Management

   • Developed by Logic Key, Inc.
   • Used in over 100 school corporations across Indiana for more than 16 years
   • Specifically designed for Indiana schools
   • Ed-Fi certified for Indiana Data Exchange
   • Harmony School Management

2. Gradelink

   • Web-based SIS that requires no special equipment to purchase or maintain
   • Indiana Ed-Fi certified for Data Exchange
   • Includes features such as one-click submission for all data and immediate status confirmation from IDOE
   • Gradelink Indiana Data Exchange

3. PowerSchool

   • Comprehensive SIS platform
   • Ed-Fi certified
   • Used by many districts in Indiana
   • PowerSchool SIS

4. Skyward

   • Offers both SMS 2.0 and Qmlativ versions
   • Ed-Fi certified for data reporting
   • Used by multiple school districts in Indiana
   • Skyward Student Information System

5. RDS (Regional Data Services)

   • Used by numerous Indiana school corporations
   • Provides student management, parent access, and online registration
   • RDS Student Management

6. Infinite Campus

   • Comprehensive K-12 student information system
   • Ed-Fi certified
   • Used by several Indiana districts
   • Infinite Campus

7. Jupiter SIS

   • Ed-Fi certified for both Data Standard v4 and Suite 3
   • Used by schools in Indiana
   • Listed on the Ed-Fi certification registry

8. Tyler Technologies SIS

   • Ed-Fi certified for Data Standard v4 (in Texas, with availability expanding)

Other K-12 Systems Used in Indiana

1. K12 Online School Platform

   • Used by Indiana Digital Learning School (INDLS)
   • Used by Indiana Gateway Digital Academy
   • K12 Online School

2. Alma

   • Cloud-based student information system
   • Provides learning management tools alongside SIS features

3. Blackbaud Student Information System

   • Used primarily by private K-12 schools in Indiana

Higher Education Student Information Systems

1. Indiana University Student Information System (SIS)

   • Central system for managing student records at all IU campuses
   • Handles admissions, financial aid, registration, advising, and more
   • IU Student Information System

2. Student Center (SIS)

   • Student-facing portal for Indiana University's SIS
   • Allows students to view grades, transcripts, schedules, and financial information
   • IU Student Center

Indiana Data Exchange Initiative

The Indiana Department of Education has implemented the Ed-Fi Data Standards and Technology Suite as the foundation of its Data Exchange project. This initiative aims to improve data transfer between schools and the IDOE through:

• Ed-Fi Operational Data Store and API
• Identities API for STN (Student Test Number) and SPN (School Personnel Number) management
• Master Data Management (MDM) for school, corporation, and network configurations
• Data Exchange Validation Portal
• Data Exchange API for student and educator data transfer

Schools must use an Ed-Fi certified student information system to connect with the Indiana Data Exchange. The IDOE began implementation with SIS vendors and pilot schools in May 2020, with all data components required to be submitted and certified via Data Exchange for the 2021-22 school year.

State Ed-ID Portal

The Indiana Department of Education also maintains the Ed-ID system, which issues education IDs to students in various educational settings, including early childhood, pre-K, and K-12, as well as to educators. This system helps track student data across different educational environments.

This list represents the major student information systems used across Indiana's educational institutions. Each system offers different features and capabilities to meet the specific needs of schools and districts throughout the state.