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Shared in our session today.

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FALCON is an informal group founded in 2024 to connect higher-education faculty and staff interested in the intersection of artificial intelligence and higher education. It has no membership fees and anyone can join.

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FALCON is an informal group founded in 2024 to connect higher-education faculty and staff interested in the intersection of artificial intelligence and higher education. It has no membership fees and anyone can join.

"The Consortium is dedicated to advancing the understanding and application of generative AI in higher education. Our mission is to foster ethical, responsible, and innovative use of AI through interdisciplinary collaboration, faculty development, and the exploration of AI's role across various academic disciplines. We aim to serve as a nexus for thought leadership, resource sharing, and policy development, ensuring academic integrity and enhancing the educational experience in the age of AI. Our commitment extends to preparing educators and institutions to adapt to and positively influence the evolving landscape of AI in education."

"The Vision of the Consortium is to empower and transform higher education through the integration of AI, fostering a future where technology enhances learning, teaching, and research in a manner that is ethical, equitable, and forward-thinking."

Our consortium is led by a steering committee and structured around Special Interest Groups (SIGs), which focus on several critical areas. They are listed below. These SIGs provide a platform for members to engage with specific AI-related topics and contribute to the advancement of our collective knowledge and practices.

Grants, Publications, and Scholarship (led by Bruce Fraser and ) - identify funding opportunities, key research questions, and provide support for FALCON members.

FALCON has one in-person meeting per year and meets virtually once per quarter (in the remaining three quarters of the year).

Each SIG will serve as both an advisory and working group, and each will decide on its own meeting schedule. SIGs will report their findings during quarterly meetings. Meetings are free.

11:00 am - 12:30 pm Eastern

Q1 - 4th Tuesday of January | Zoom

Q2 - 4th Tuesday of April | Zoom

Q4 - 4th Tuesday of October | Zoom

Leslie Rios, Santa Fe College

Bruce Fraser, Indian River State College

Anna Haney-Withrow, Florida SouthWestern State College

Rozalind Jester, Florida SouthWestern State College

Anyone can join FALCON. It has no membership fees. to become a FALCON member (Look for the "Join Group" button, which is visible if you are logged in to Google).

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Florida A.I. Learning Consortium (FALCON)
FALCON is an informal group founded in 2024 to connect higher-education faculty and staff interested in the intersection of artificial intelligence and higher education. It has no membership fees and anyone can join.
FALCON Mission Statement
“The Consortium

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FLUF Test for Critical Evaluation of AI generative results

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FLUF Test for Critical Evaluation of AI generative results

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Link to today's presentation https://docs.google.com/presentation/d/1eEey8SeN7ufd3uMP5u-nbZ_LNBQK--wvVLwWRvjJjrE/edit?usp=sharing

Recent presentation on FLUF, CRAAP, iSearch and more!

https://docs.google.com/document/d/1u2FKn4RWJ5M5YzWi1tppQ9YVn5HO5S8G/edit?usp=sharing&ouid=114777895029122273519&rtpof=true&sd=true

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Everyone is talking about Artificial Intelligence. With tools like ChatGPT and Copilot at our fingertips, it's as easy as a Google search! So, this gets us thinking - if we are going to embrace the power of AI, how do we critically evaluate AI generated content? Enter - the FLUF test.​

The FLUF Test is a framework for critically evaluating content generated through artificial intelligence. The framework encourages the user to look critically at format, language, usability, and fanfare when considering the outputs of the AI generated results. AI generated results can be flowery or inaccurate - or repetitive for example. Using these tools means becoming educated about their power, usefulness, and shortcomings.

In 2023, I developed the FLUF Test, based on many years as a teacher of information and digital media literacy skills across PK20 environments. With the FLUF test, you are looking specifically at format, language, usability and fanfare and the indicators for each. The goal is to have a result that has zero FLUF, or zero infractions in the generated results.

L for Language , you are looking at tone, phrasing, and repetition. If the tone sounds too business-like, then the score is a PLUS and needs to be regenerated. Usability Look For's Format Look For's
In the area of F for

Tone - If it lacks personal style, tone, or human elements, it gets a +.

Phrasing - If the syntax or semantics are off, or it presents information in an awkward way, it gets a +.

Format, you are analyzing layout or length. Analyze your result - is the length too long? Does it use MLA when you wanted APA format? If there is an issue, score the rubric with a "plus" - indicating an infraction. A Plus means it needs to be regenerated or tweaked.

Layout - If it doesn't follow the formal writing patterns or format, it gets a +.

Length - If there are lots of extra words to extend the word count, it gets a +.

U is for Usability, where you want to know if the information is valid and reliable - and are looking for credibility and consistency.

Consistency - if there are inconsistencies in content it gets a +

Credibility - if you cannot determine whether there are credible references, information cannot be authenticated or validated, or it lacks citations/sources for documentation, then it gets a +.

Finally, we have F for Fanfare , we'd look for visual appeal and layout - but with words we are looking for academic writing that is free from anecdotes and jargon. This doesn't mean we don't want technical vocabulary, but it does mean we don't want to use cliches. Here, you are looking for the writing to present information in an informative way and specific way, in the style you intend, and make connections across content. Do the results deepen the conversation by comparing or contrasting information? presenting metaphors or analogies? sharing examples, stories, cases, or scenarios?

Repetition - if the passage lacks succinct presentation of ideas, or has run-on sentences or repetition of ideas, thoughts, and/or phrases, it gets a +.

Depending on the intention of the writing:

Anecdotes - the absence of human examples, analogies, metaphors, or comparisons gets a +

Jargon - jargon is another form of repetition, cliché, or assumption and can come in the form of a condescending tone. If the jargon is presented in this way without specificity, it gets a +

Any infractions (pluses) indicate the need to regenerate or re-prompt the results with the AI generator. Starting with a good prompt will eliminate infractions and help get to "Zero FLUF".

Use the 2-page template to create a prompt guided by the FLUF indicators for your AI tool, then use the FLUF Test to evaluate results.

There are currently no critical evaluation frameworks around artificial intelligence.With a background in library science, the literature review centered on information literacy and frameworks for critical evaluation of online information.80% research and regeneration of online sources
FLUF Test: A Framework for Critical Evaluation of Content Generated with Artificial Intelligence Here are some of the underpinnings for this work: Dr. Jennifer L. Parker
CC BY-NC-SA 4.0
The 80/20 Rule (Pareto Principle)
The balance between human insight and technological capability

20% human critique, creativity, and culmination to create a final output
Frameworks for Critical Evaluation of Online Resources
CRAAP (Blakeslee, 2004) - currency, relevance, authority, accuracy, purpose
CARRDSS (Valenza, 2004) - credibility, accuracy, reliability, relevance, date, sources, scope
SIFT (Caulfield, 2019) - stop, investigate, find, trace
5 Key Questions (Thorman & Jolls, 2003) - creator, techniques, perceptions, bias, purpose

The FLUF test is presented to encourage users to expand their media and information literacy, and extend their use of critical evaluation of all information - no matter how it is obtained. For an overview of the rationale leading to the FLUF Test framework and a detailed walk through of the FLUF experience, view the presentation.

For more information about the FLUF Test, templates, or a consultation, contact me.

​ © 2023 by is licensed under

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FLUF Test: A Framework for Critical Evaluation of Content Generated with Artificial Intelligence © 2023 by Dr. Jennifer L. Parker is licensed under CC BY-NC-SA 4.0 .

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Vetting AI Tools for K-12 Schools: Inforgraphic

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Vetting AI Tools for K-12 Schools: Inforgraphic

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White Colorful Modern Timeline Design Process Infographic

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MergeCube + HoverCam = Intelligent HoverMerge

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MergeCube + HoverCam = Intelligent HoverMerge

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Vetting AI Tools for K-12 Schools: Power Point

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Vetting AI Tools for K-12 Schools: Power Point

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Principles | AI Guidance for Schools Toolkit | TeachAI

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Seven principles for developing AI guidance in schools including a description, real-world examples, resources, considerations and sample language to develop policies and best practices in educational contexts.

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Creating an action plan to vet AI tools for a K-12 school involves several key steps. Here's a comprehensive action plan:

1. Establish a Committee

Objective: Form a diverse committee to oversee the vetting process.

Participants: Include teachers, IT staff, school administrators, parents, and students (if appropriate).

Roles: Assign roles such as project lead, technical advisor, curriculum expert, and data privacy officer.

2. Define Objectives and Requirements

Objective: Clearly outline what you aim to achieve with AI tools.

Educational Goals: Improve learning outcomes, personalize learning, enhance administrative efficiency, etc.

Technical Requirements: Compatibility with existing systems, ease of use, scalability, etc.

Ethical Standards: Ensure tools promote equity, transparency, and student well-being.

3. Conduct Market Research

Objective: Identify potential AI tools and gather information.

Sources: Use industry reports, educational technology conferences, online reviews, and peer recommendations.

Shortlisting: Create a list of tools that meet your initial criteria.

4. Evaluate Data Privacy and Security

Objective: Ensure tools comply with data protection regulations.

Standards: Check compliance with FERPA, COPPA, and local data privacy laws.

Vendor Policies: Review vendors’ data handling practices, storage security, and user consent procedures.

5. Assess Educational Impact

Objective: Determine the potential educational benefits and drawbacks.

Pilot Programs: Implement small-scale pilots in a few classrooms to gather initial feedback.

Metrics: Use standardized tests, student progress tracking, and teacher assessments to measure impact.

6. Review Ethical Considerations

Objective: Ensure the ethical use of AI tools.

Bias and Fairness: Check for algorithmic bias and ensure tools serve all student demographics equitably.

Transparency: Ensure the AI’s decision-making process is understandable and explainable.

7. Gather Stakeholder Feedback

Objective: Collect input from all affected parties.

Surveys and Interviews: Conduct surveys with teachers, students, and parents to gather qualitative and quantitative feedback.

Focus Groups: Hold discussions with different stakeholder groups to delve deeper into specific concerns and suggestions.

8. Cost-Benefit Analysis

Objective: Evaluate the financial viability of AI tools.

Budget: Compare costs with the allocated budget for educational technology.

ROI: Consider long-term benefits versus initial and ongoing costs.

9. Professional Development

Objective: Prepare staff for effective use of AI tools.

Training Programs: Develop comprehensive training for teachers and administrators.

Ongoing Support: Provide continuous professional development and technical support.

10. Make a Decision

Objective: Select the most appropriate AI tools based on thorough evaluation.

Final Review: Have the committee review all data and make an informed decision.

Board Approval: Present findings and recommendations to the school board for final approval.

11. Implementation and Monitoring

Objective: Ensure smooth implementation and continuous improvement.

Rollout Plan: Develop a phased implementation plan with clear timelines.

Monitoring: Continuously monitor the tool’s performance and impact on students and staff.

Feedback Loop: Establish a process for ongoing feedback and make adjustments as needed.

12. Documentation and Reporting

Objective: Maintain records and report on the process and outcomes.

Documentation: Keep detailed records of the vetting process, decisions made, and implementation steps.

Reporting: Regularly update stakeholders on the progress and outcomes of the AI tool’s integration.

By following this action plan, you can ensure a thorough and thoughtful approach to vetting AI tools for your K-12 school district, ultimately enhancing educational outcomes while safeguarding student data and promoting ethical practices.

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Assessing AI for Classroom Use Rubric and Checklist

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The Co-CreatED Teacher Growth Platform helps teachers & administrators turn their most important strategic priorities into actions that get results

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AI resources from a high school district

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AI ideas to consider when vetting, Nebo's model

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AI ideas to consider when vetting, Nebo's model

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The first sheet has some of my thoughts that I shared with our AI committee.

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Sharing - Classroom AI Software RFP Evaluation Scoresheet

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Ohio’s new AI toolkit seeks to guide K-12 policy | StateScoop

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Ohio and the nonprofit AI Education Project published new resources for educators seeking to develop AI policies for their classrooms.

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AI & Ethics Presentation by Dr. Torrey Trust

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Today, many priorities for improvements to teaching and learning are unmet. Educators seek technology-enhanced approaches addressing these priorities that would be safe, effective, and scalable. Like all of us, educators use AI-powered services in their everyday lives, such as voice assistants in their homes; tools that can correct grammar, complete sentences, and write essays; and automated trip planning on their phones. As a result, educators see opportunities to use AI-powered capabilities like speech recognition to increase the support available to students with disabilities, multilingual learners, and others who could benefit from greater adaptivity and personalization in digital tools for learning. They are further exploring how AI can enable writing or improving lessons, as well as their process for finding, choosing, and adapting material for use in their lessons.

Educators are also aware of new risks. Useful, powerful functionality can also be accompanied with new data privacy and security risks. Educators recognize that AI can automatically produce output that is inappropriate or wrong. They are wary that the associations or automations created by AI may amplify unwanted biases. They have noted new ways in which students may represent others' work as their own. They are well-aware of "teachable moments" and pedagogical strategies that a human teacher can address but are undetected or misunderstood by AI models. They worry whether recommendations suggested by an algorithm would be fair. Educators' concerns are manifold.

Everyone in education has a responsibility to harness the good to serve educational priorities while also protecting against the dangers that may arise as a result of AI being integrated in educational technology.

NEW!! Report on AI and the Future of Teaching & Learning

Key Insights

AI enables new forms of interaction. Students and teachers can speak, gesture, sketch, and use other natural human modes of communication to interact with a computational resource and each other. AI can generate human-like responses, as well. These new forms of action may provide supports to students with disabilities.

AI can help educators address variability in student learning. With AI, designers can anticipate and address the long tail of variations in how students can successfully learn-whereas traditional curricular resources were designed to teach to the middle or most common learning pathways. For example, AI-enabled educational technology may be deployed to adapt to each student's English language abilities with greater support for the range of skills and needs among English learners.

AI supports powerful forms of adaptivity. Conventional technologies adapt based upon the correctness of student answers. AI enables adapting to a student's learning process as it unfolds step-by-step, not simply providing feedback on right or wrong answers. Specific adaptations may enable students to continue strong progress in a curriculum by working with their strengths and working around obstacles.

AI can enhance feedback loops. AI can increase the quality and quantity of feedback provided to students and teachers, as well as suggesting resources to advance their teaching and learning.

AI can support educators. Educators can be involved in designing AI-enabled tools to make their jobs better and to enable them to better engage and support their students.

Recommendations: Call to Action for Education Leaders

Recommendation #1: Emphasize Humans in the Loop

We reject the notion of AI as replacing teachers. Teachers and other people must be "in the loop" whenever AI is applied in order to notice patterns and automate educational processes. We call upon all constituents to adopt Humans-in-the-Loop as a key criteria.

Recommendation #2: Align AI Models to a Shared Vision for Education

We call upon educational decision makers, researchers, and evaluators to determine the quality of an educational technology based not only on outcomes, but also based on the degree to which the models at the heart of the AI tools and systems align to a shared vision for teaching and learning. The figure to the right describes the important qualities of AI models for education leaders to consider.

Recommendation #3: Design Using Modern Learning Principles

Achieving effective systems requires more than processing "big data"-it requires more than data science. Applications of AI must be based on established, modern learning principles, the wisdom of educational practitioners, and should leverage the expertise in the educational assessment community around detecting bias and improving fairness. Going forward, we also must seek to create AI systems that are culturally responsive and culturally sustaining, leveraging the growth of published techniques for doing so. Further, most early AI systems had few specific supports for students with disabilities and English learners and we must ensure that AI-enabled learning resources are intentionally inclusive of these students.

Recommendation #4: Prioritize Strengthening Trust

Technology can only help us to achieve educational objectives when we trust it. Yet, we learned through a series of public listening sessions that distrust of educational technology and AI is commonplace. Because trust develops as people meet and relate to each other, we call for a focus on building trust and establishing criteria for trustworthiness of emerging educational technologies within the associations, convenings, and professional organizations that bring educators, innovators, researchers, and policymakers together.

Recommendation #5: Inform and Involve Educators

We call on educational leaders to prioritize informing and involving educational constituents so they are prepared to investigate how and when AI fits specific teaching and learning needs, and what risks may rise. Now is the time to show the respect and value we hold for educators by informing and involving them in every step of the process of designing, developing, testing, improving, adopting, and managing AI-enabled educational technology. This includes involving educators in reviewing existing AI-enabled systems, tools, and data use in schools, designing new applications of AI based on teacher input, carrying out pilot evaluations of proposed new instructional tools, collaborating with developers to increase the trustworthiness of the deployed system, and raising issues about risks and unexpected consequences as the system is implemented.

Recommendation #6: Focus R&D on Addressing Context and Enhancing Trust and Safety

Research that focuses on how AI-enabled systems can adapt to context (diversity among learners, variability in instructional approaches, differences in educational settings) is essential to answering the question "Do specific applications of AI work in education, and if so, for whom and under what conditions?" We call upon researchers and their funders to prioritize investigations of how AI can address the long tail of learning variability and to seek advances in how AI can incorporate contextual considerations when detecting patterns and recommending options to students and teachers. Further, researchers should accelerate their attention to how to enhance trust and safety in AI-enabled systems for education.

Recommendation #7: Develop Education-Specific Guidelines and Guardrails

Data privacy regulation already covers educational technology; further, data security is already a priority of school educational technology leaders. Modifications and enhancements to the status quo will be required to address the new capabilities alongside the risks of AI. We call for involvement of all perspectives in the ecosystem to define a set of guidelines (such as voluntary disclosures and technology procurement checklists) and guardrails (such as enhancements to existing regulations or additional requirements) so that we can achieve safe and effective AI for education.

Listening Sessions

The U.S. Department of Education's Office of Educational Technology, with support from Digital Promise, held listening sessions about Artificial Intelligence (AI). We connected with all constituents involved in making decisions about technology in education, including but not limited to teachers, educational leaders, students, parents, technologists, researchers, and policy makers.

The goal of these listening sessions were to gather input, ideas, and engage in conversations that will help the Department shape a vision for AI policy that is inclusive of cutting-edge research and practices while also informed by the opportunities and risks.

Blog Posts

Need More Info?

Contact ed.tech@ed.gov with questions.

Please Include "OET AI" in the Subject Line

For a print-out and summary of the Artificial Intelligence Initiative, please review our AI One-pager

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Artificial Intelligence - Office of Educational Technology

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OET is working to develop policies and supports focused on the effective, safe, and fair use of AI-enabled educational technology.

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NCDPI Generative AI Implementation Recommendations and Considerations for PK-13 Public Schools (1).pdf