Here’s five ways AI can transform your campus:

1. Streamlining administrative processes.

Nobody likes doing admin – even the people who are specifically hired to do it. AI can step in and take an enormous amount of admin work off your shoulders, letting your staff focus on the things that really matter, rather than being stuck crunching data all day. For instance, AI can help out with:

• Scheduling – scheduling appointments is a pain in the best of circumstances, and leads to the email chain of doom in the worst of circumstances. AI can step in and take care of scheduling by keeping track of everyone’s calendars, proposing dates and times, keeping track of conflicts, and sending out event invites.

• Grading – one of the worst parts of academia, grading is also a common area where biases and errors can occur. AI can shoulder the heavy work, reviewing student assignments for accuracy, helping instructors create grading rubrics, and even providing thoughtful feedback on writing assignments.
  
  What’s more, AI can help instructors eliminate some of the unconscious biases that may arise in grading, making the whole process more equitable.

• Admissions processing – similarly, AI can help out with admissions processing, reviewing student applications for suitability against a set of criteria, and saving time by only putting the most qualified candidates in front of the admissions board. Again, AI can also help eliminate unconscious bias, as it simply reviews admissions packets according to the criteria you set up, without the potential biases of a reviewer.

2. Improving student support services.

Student support is one of the most important elements of the college experience, but it’s also one of the most expensive and complicated.

Institutions can use AI to help make this process easier and more effective for everyone involved, leading to a better campus experience for students while saving resources for the university.

For instance, AI could be used to help with:

• Student support chat bots – chatbots can provide 24/7 support that would be unrealistic in a historical university setting, allowing students to get answers to common questions and guiding them through administrative processes.

• Identifying at-risk students – AI algorithms can be set up to identify at-risk students, reviewing their academic performance and engagement in real-time and ensuring that students don’t slip through the cracks.

• Providing career guidance – institutions can even use AI tools to provide career guidance and counseling, letting students “talk” to the AI to get a sense of what types of jobs might be a good fit for them. They can also use AI tools to help build their resumes, generate cover letters, and even practice job interviews!

3. Personalizing learning experiences.

One of the biggest benefits of AI is how it allows for individual personalization without taking up the time and resources of the instructors. AI platforms can adapt content based on students’ performance and learning style, creating custom learning paths that lead to better educational outcomes.

We’ve been excited to see universities using AI to:

• Create adaptive learning platforms – it would be amazing if every student could have their own individual learning plan, but that’s just now realistic with the traditional university lecture model. AI can act as an augmentation to that model, letting instructors input their lessons and then adapting those lessons to the needs of each individual student.

• Develop tutoring systems – historically  tutoring was only accessible to those with the time and money to spend on it, but AI tutors can be used to provide personalized instruction in a wide variety of fields, and help students get a handle on complex topics without having to take up their instructors’ time.

• Use predictive analytics to predict students’ performance – a fantastic instructor has their finger on the pulse of how each student in their class is doing, but with class sizes increasing, that’s becoming more and more difficult to do. Predictive analytics can help out, flagging students for instructors when they start to become at-risk, and recommending targeted support.

4. Enhancing accessibility and inclusivity.

One of the best things about AI is how, unlike many other technologies in their early adoption phases, it’s extremely accessible, working with screen readers and providing voice-only options for engagement.

Institutions can leverage this huge upside of AI to make their own campuses more accessible and inclusive by:

• Supporting diverse learning styles – AI allows students to interact with course materials in ways that make the most sense for them and their brains, rather than having to try to fit into the model the instructor has set up.

• Easily integrating students with disabilities – AI tools offer instant, high-quality assistance with text-to-speech and speech-to-text, making it easier for students to access materials regardless of any disability.

• Allowing for real-time translation – AI tools can translate lectures and materials in real-time, letting students learn in the language they are most comfortable with.

5. Innovating pedagogical approaches.

What would it be like if a student could learn about the Declaration of Independence by having a conversation with Thomas Jefferson, or get support in learning math by playing a game designed specifically for them?

The possibilities are truly endless with AI, and instructors are starting to use it to create new and innovative pedagogical approaches by:

• Developing creative new lesson plans and learning experiences – AI lets instructors bring any number of new elements into their lesson plans with the touch of a button. From AI-assisted peer review to digital art collaboration, it’s all there.

• Facilitating collaboration and interdisciplinary learning – collaborative AI platforms like BoodleBox let students easily work with each other, AI tools, and even students in different disciplines, all from one simple and centralized platform.

• Creating an interactive learning environment – the days of the lecturer talking at students are long gone, AI allows for an unprecedented level of interaction in learning, making it much more exciting – and effective – for students and faculty.

And honestly, this is just the beginning. AI is improving by leaps and bounds every day, and academic institutions are just starting to really understand the transformations they can unlock on their campuses by partnering with it.

As an AI company that focuses specifically on integrating responsible, ethical, and effective AI into academia, we’d love to support you in bringing this kind of transformation to your campus.
Find out more about how BoodleBox can supercharge your institution – and try it for free! – here.

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We’re thrilled to announce the launch of several new education-specific bots on BoodleBox, currently in beta testing. We believe these tools will significantly enhance your teaching experience and streamline various aspects of course preparation, material creation, and assessment.

We invite you to try out these new bots and provide us with your valuable feedback. Your input will be crucial in refining and improving these tools to better serve your needs. Here’s a list of the new education-specific bots now available:

Class Preparation

EduBot Student Learning Objectives on BoodleBox | BoodleBox

EduBot Course Syllabus on BoodleBox | BoodleBox

EduBot Class Assignment on BoodleBox | BoodleBox

EduBot Class Lesson Plans on BoodleBox | BoodleBox

EduBot Slide Text on BoodleBox | BoodleBox

EduBot Class Assignment (Authentic) on BoodleBox | BoodleBox

EduBot Metacognition on BoodleBox | BoodleBox

EduBot Accessibility on BoodleBox | BoodleBox

Class Materials

EduBot Handout on BoodleBox | BoodleBox

EduBot One Pager on BoodleBox | BoodleBox

EduBot Quiz on BoodleBox | BoodleBox

EduBot Debate on BoodleBox | BoodleBox

Classwork Assessment

EduBot Rubric on BoodleBox | BoodleBox

EduBot Feedback on BoodleBox | BoodleBox

EduBot Learning Gaps on BoodleBox | BoodleBox

Specialized

EduBot STEM Lab on BoodleBox | BoodleBox

Adversary Bot on BoodleBox | BoodleBox
Plain Language Bot on BoodleBox | BoodleBox

You can also access the bots in the ChatBar by typing “@edu” (AdversaryBot is “@adversary”)

We encourage you to explore these new tools and experiment with how they can enhance your teaching methods and course management.

To provide feedback, please email us at support@boodle.ai. Your feedback is invaluable to us, so please don’t hesitate to share your thoughts, suggestions, or any issues you encounter.

Thank you for your continued support and participation in making BoodleBox an even more powerful platform for education. We look forward to hearing about your experiences with these new education-centered bots!

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General Purpose Foundation Models.

Your do it all conversational AI assistants. They are all limited to the knowledge they were trained with, which often has a data cut off.

ChatGPT 5.1 is OpenAI’s newest model, with enhanced reasoning capabilities, improved factual accuracy, and more nuanced understanding of complex instructions. Use ChatGPT 5.1 when you need the most sophisticated problem-solving, detailed analysis, or highly accurate information.

Other OpenAI models available:

• ChatGPT 5 – Advanced model that adapts between fast responses and deep reasoning based on task complexity.
• ChatGPT-4.1 – Best for large document analysis, a second opinion, and/or something in nice formatting
• ChatGPT-4o – Choose this for tasks involving image analysis or when you need fast, versatile responses across text and visual inputs.
• ChatGPT-4o-mini – Use for high-volume, straightforward tasks where speed and cost-efficiency matter more than advanced capabilities.
• ChatGPT-o3 – Select for complex research and analysis that requires step-by-step reasoning and integrated tool use.
• ChatGPT-o3-mini – Ideal for STEM problems, mathematics, and coding tasks that need precision but not extensive context.
• ChatGPT-o4-mini – Use for reasoning tasks that also require image analysis or when you need the latest reasoning capabilities in a compact model.

Claude 4.5 Sonnet is Anthropic’s latest and most advanced model, designed for coding, complex agents, and tasks requiring advanced reasoning, math, and long-term performance. It excels at code generation, debugging, and long-running computer-based tasks, making it a powerful tool for developers and for autonomously executing business-critical workflows. Use Claude 4.5 for your most demanding technical challenges, sophisticated agentic systems, and projects requiring sustained high-level reasoning.

Other Anthropic models available:

• Claude 4.5 Haiku – Fast, efficient model optimized for speed while maintaining strong performance on everyday tasks and rapid iterations.
• Claude 4.5 Opus – Anthropic’s most powerful frontier model designed for the most demanding intellectual tasks requiring maximum reasoning and deep reasoning.
• Caude 4.1 Opus – Advanced reasoning model for complex challenges, strategic decisions, and sophisticated multi-step problem-solving.
• Claude 4.0 Sonnet – Balanced model offering strong capabilities with high throughput for production environments.

Gemini-3-Pro is Google’s newest and most advanced AI model with enhanced reasoning capabilities, improved multimodal understanding, and superior performance across complex tasks. Use Gemini 3 Pro for the most demanding analytical work, advanced research, and sophisticated problem-solving.

Other Gemini models available:

• Gemini 3 Flash – Fast, efficient version of Google’s latest model optimized for speed while maintaining strong capabilities across text and visual inputs.
• Gemini 2.5 Pro – Powerful model with large context window for processing extensive content and information.
• Gemini-2.0-Flash – Google’s fast, efficient model designed for quick responses across various media types with improved performance and speed.

Llama-3.3 is Meta’s advanced open-source language model notable for its efficiency, accuracy, multilingual capabilities, and adaptability in generating human-like text based on the input provided. Use Llama 3.3 for versatile text generation with the reliability of Meta’s open-source approach.

Other Llama models available:

• Llama-4-Maverick – Advanced model delivering exceptional performance across reasoning, coding, and creative tasks with enterprise-grade capabilities.
• Llama-4-Scout – Lightweight yet powerful model designed for efficiency and quick responses while maintaining strong performance.

Codestral is Mistral’s specialized coding-focused language model that excels at understanding, generating, and debugging code across multiple programming languages. Use Codestral for programming assistance, code optimization, and technical problem-solving.

Other Mistral models available:

• Mistral-Large – Mistral’s flagship model for complex reasoning and analysis tasks requiring broad capabilities.
• Pixtral – Mistral’s multimodal model capable of processing both text and images for diverse visual and textual tasks.

Nova-Pro is Amazon’s versatile foundation model designed to handle a wide range of tasks with strong reasoning capabilities. Use Nova Pro for balanced performance across various applications requiring both creativity and analytical thinking.

Other Amazon models available:

• Nova-Micro – ompact model optimized for speed and efficiency in simple tasks with minimal resource requirements.
• Nova-Lite – Mid-tier model balancing capability and resource efficiency for everyday applications.

Nemotron is NVIDIA’s efficient open-source model designed for fast, accurate responses with strong reasoning capabilities. It can handle very large amounts of text and works in over 20 languages. Use Nemotron when you need quick, reliable AI assistance with complex reasoning tasks or when working with multiple languages.

Web-enabled research bots.

The bots you turn to when you want to find relevant, real-time information from the internet.

Perplexity is Perplexity’s innovative AI-powered search engine that goes beyond traditional search by providing fast, detailed, citation-backed answers synthesized from multiple sources. Use Perplexity when you need real-time information or research. When you want to fact-check information, end with Perplexity. Note: click the “globe” icon to see citations.

Tavily is an AI-powered search API built specifically for developers to provide large language models with real-time, accurate, and relevant information from the web. Use Tavily when you need real-time web information, want to minimize AI hallucinations with grounded data, and require reliable external information sources for research and fact-checking.

Bots for Research.

Perplexity. See above. Use Perplexity to start your research.

Semantic Scholar is a research assistant powered by Semantic Scholar API that searches 214 million scientific papers from 79 million authors and provides intelligent insights into research trends, citation networks, and interdisciplinary connections. Use Semantic Scholar when you want to do academic, scholarly research and need sources and citations.

Text to Image Generators.

Flux-2-Pro is Black Forest Labs’ latest image generator that stands out for its ability to create high-quality images from textual descriptions, offering unparalleled detail, prompt adherence, and style diversity. Use Flux 2 Pro when you need rapid, high-quality image generation, especially of people.

SDXL is Stable Diffusion’s latest AI image generation model that can generate realistic faces, legible text within images, and diverse art styles with excellent image quality. Use SDXL when you need versatile image generation with strong text rendering and realistic people.

GPT Image 1 is OpenAI’s advanced text-to-image model that specializes in creating diverse, high-quality images from text descriptions with remarkable creativity and artistic flexibility. Known for its strong understanding of complex prompts and ability to generate images in various artistic styles—from photorealistic to abstract. Use GPT Image 1 when you need consistent, detailed results with excellent composition and color balance.

Ideogram V3 is Ideogram’s innovative AI image generator that excels in creating images with accurate and legible text integration. Use Ideogram V3 when you need images containing readable text elements.

Nano Banana Pro is Google’s advanced image generation model optimized for high-quality visuals with enhanced capabilities and improved text rendering. Use Nano Banana Pro when you need superior image quality with fast turnaround and reliable text integration.

Other Google models available:

• Nano Banana –  Fast, efficient image generation model optimized for speed and basic visual creation needs.
• Imagen 3.0 – Specialized for creating highly detailed and contextually accurate images from scratch with photorealistic rendering and strong prompt adherence.

Bots for When You Need a Hand.

BoodleBot automatically selects the best AI model for your specific task or question, routing your request to the most appropriate assistant based on the content and complexity of your input. Use BoodleBot when you want optimal results without having to choose which model to use.

HelpBot helps users navigate the BoodleBox platform, recommends specific AI models and assistants to solve specific problems, and provides guidance on how generative AI can be effectively used for various tasks and use cases. Use HelpBot if you have a question about GenAI in general or BoodleBox in particular.

PromptBot helps users craft effective prompts to guide other AI models in generating desired outputs and explains its rationale. Use PromptBot when you want your prompt to be “chef’s kiss.”

The post BoodleBox’s Guide to GenAI Models & Bots first appeared on BoodleBox.

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1. If you’re not getting the expected results with the standard knowledge integration.
2. When the entire content of the document(s) attached to the chat is necessary to generate an accurate response.

Examples when “Enable Knowledge to Context” is not required:

1. Quick fact-checking: User: “Who wrote this report?” Attached knowledge: Contains a multi-page report. Explanation: The AI can quickly retrieve this simple fact without needing full context.

2. Summarizing a short article: User: “Summarize the main points of the article about climate change.” Attached knowledge: Contains a 500-word article on recent climate change findings. Explanation: The AI can effectively summarize the key points without needing the entire document in context.

3. Answering specific questions from a larger document: User: “What are the three main benefits of exercise mentioned in the health guide?” Attached knowledge: Contains a comprehensive health and fitness guide. Explanation: The AI can locate and extract the specific information without requiring the entire guide in context.

Examples when “Enable Knowledge to Context” is required:

1. Complex analysis of a lengthy document: User: “Analyze the themes and character development throughout the entire novel ‘To Kill a Mockingbird’.” Attached knowledge: Contains the full text of “To Kill a Mockingbird.” Explanation: The AI needs access to the entire novel to provide a comprehensive analysis.

2. Identifying patterns across multiple documents: User: “Compare and contrast the economic policies proposed in the last three State of the Union addresses.” Attached knowledge: Contains full transcripts of the last three State of the Union speeches. Explanation: The AI needs to access and analyze all three speeches in their entirety to identify patterns and make comparisons.

3. Generating a detailed report based on extensive data: User: “Create a comprehensive report on global renewable energy trends over the past decade, including regional variations and future projections.” Attached knowledge: Contains multiple lengthy reports and datasets on renewable energy from various sources. Explanation: The AI needs access to all the relevant data and reports to generate an accurate and detailed analysis.

4. Answering questions that require understanding of context and nuance: User: “Explain how the author’s personal experiences influenced the themes in their autobiography.” Attached knowledge: Contains the full text of the autobiography. Explanation: The AI needs to understand the entire narrative and context of the author’s life to draw accurate connections between experiences and themes.

In these cases where “Enable Knowledge to Context” is required, the AI needs access to the full context of the attached knowledge to provide accurate, comprehensive, and nuanced responses.

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The AI Leadership Council has two primary missions:

1. Provide thought leadership to the community at large on collaborative AI in higher education
2. Directly influence the direction of BoodleBox as it strives to become the leading AI solution for Higher Education

The council is comprised of 20 esteemed educators from various institutions, including:

• Mr. Derek Abrams, Associate Professor of Practice, Entrepreneurship, & Innovation
• Dr. Berkley Baker, Professor of Practice, Georgia State University
• Dr. David Barnes, Former Chief AI Ethics Officer, US Army Artificial Intelligence Integration Center
• Dr. Thomas Conway, Professor & Program Coordinator, Regulatory Affairs – Sciences, Algonquin College of Applied Arts & Technology
• Dr. Lance Cummings, Associate Professor of Professional Writing, UNC Wilmington
• Dr. Elisabeth Dellegrazie, Assistant Dean of Faculty, Curriculum, and Instruction, Concordia University Chicago
• Dr. Ron Dufresne, Professor of Management, Saint Joseph’s University
• Dr. Dean Goon, Dean of Academic Innovation, Embry-Riddle Aeronautical University
• Dr. Jason Gulya, Professor of English, Chair, AI Council, Berkeley College
• Mr. Jamil Jaffer, Assistant Professor of Law, George Mason University
• Dr. Alyse Jordan, Head of Research, Engagement & Learning, Lamar University
• Dr. Ryan Langan, Associate Professor of Marketing, University of San Francisco
• Ms. Cheney Luttich, English Instructor, Southeast Community College
• Mr. Ray Pedden, Strategy and Innovation Consultant, Center for Care Innovations
• Ms. Natalia Pierson, Associate Director of Prospect Management Research, Norfolk State University
• Dr. Michael Rodriguez, Associate Professor, Campbell University
• Dr. Jordan Shapiro, Associate Professor – Instructional, College of Liberal Arts, Temple University
• Ms. Lara Taylor, Instructional Technology Specialist, Technology and Innovation Unit, Mississippi State University
• Dr. Kevin Trainor, Assistant Professor of Practice, Marquette University
• Dr. Denise Turley, Assistant Professor of Business, University of the Southwest

BoodleBox’s partnership with these educators demonstrates its commitment to understanding the needs of higher education institutions, supporting faculty and professors as they responsibly integrate AI in the classroom, and preparing students for the AI-ready workforce. By working closely with the AI Leadership Council, BoodleBox aims to develop an innovative solution that enhances the learning experience for students and streamlines faculty processes, such as lesson planning and grading, while addressing the ethical considerations surrounding AI in education.

We are thrilled to partner with such incredible educational leaders across a diverse group of institutions.” Their expertise and insights will be invaluable as we work together to shape the future of AI in higher education. Through this collaboration, we will continue to build a world-class tool to enable professors and students to responsibly collaborate with GenAI in their classrooms and in their classwork.

France Hoang, Co-founder and CEO at BoodleBox.

If you are interested in becoming a member of the AI Leadership Council and contributing to the future of AI in higher education, we encourage you to apply today. Visit https://boodlebox.ai/edu/council/ to learn more about the council and submit your application.
If you are interested in exploring a pilot of BoodleBox for your class, division, or campus, please apply to our pilot program by visiting https://boodlebox.ai/edu/pilot/.  

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The Goal of Collaborative Generative AI in Education

The goal of collaborative GenAI in education is not merely to teach students how to use AI as a tool, but rather to develop their understanding of AI’s capabilities, limitations, and potential risks and benefits. Through hands-on experience with AI technologies like ChatGPT, Claude, Gemini, and Perplexity, students can gain practical knowledge and learn to critically evaluate AI-generated insights and recommendations.

Challenges in Adopting Generative AI in the Classroom

However, the adoption of Generative AI in the classroom is not without its challenges. Ed Weekly found that a lack of technical skills amongst educators was a top barrier to using AI tools for teaching. To address this issue, platforms like BoodleBox are emerging, providing a user-friendly interface and robust feature set tailored to the unique needs of educators for simplicity and collaboration.

The Benefits of Collaborative Generative AI in Higher Education

When students and professors can collaborate with generative AI and each other, it fosters an environment for faster AI adoption, better results, and increased engagement through learning and understanding the best practices for AI usage. With BoodleBox’s collaborative approach to AI, it allows all participants to work together with multiple people, multiple AI bots, and documents all in one chat experience. This way of working together on AI-powered projects, allows students to develop the interpersonal skills and technical expertise necessary to build trust, communicate effectively, and manage the unique challenges that arise in human-AI teams.

The Benefits of Collaborative Generative AI in Higher Education

Through hands-on collaboration with generative AI in the classroom setting, it delivers significant benefit to their in and out of classroom experience through:

1. Enhanced student collaboration
2. Improved productivity
3. Accessible adoption of Generative AI technologies
4. Hands-on learning opportunities that instill responsible AI usage and ethics

This practical use of AI prepares students an added skillset when approaching the job market – equipping them with the skills needed for critical thinking, while enabling them to engage in responsible and effective AI usage regardless of industry.

Preparing the Next Generation of AI Leaders Through AI Education

As the demand for AI-proficient professionals continues to grow, higher education institutions that embrace collaborative generative AI will be at the forefront of producing graduates capable of leading human-AI collaboration. By focusing on developing students’ understanding of AI, fostering their leadership skills, and providing them with an ethical framework, educators can ensure that the next generation is prepared to tackle the complex challenges of an AI-driven world.

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Microsoft and LinkedIn just released their 2024 Work Trend Index (AI at Work Is Here. Now Comes the Hard Part (microsoft.com)) and 3 findings stood out:

• “75% of knowledge workers use AI at work today, and 46% of users started using it less than six months ago.”
• “78% of AI users are bringing their own AI tools to work (BYOAI)—it’s even more common at small and medium-sized companies (80%).
• “52% of people who use AI at work are reluctant to admit to using it for their most important tasks.”

What this report means for organizations

For every 10 knowledge workers on your team, it’s likely: 

• 8 workers are already using AI at work (and 4 of those started less than 6 months ago)
• 6 workers are bringing their own AI tools (BYOAI)
• 4 workers are reluctant to admit they are using AI for important tasks

Put another way: For every person in your team who is using AI openly, there’s likely another person who’s using AI quietly … and doing BYOAI. 

BYOAI raises a number of risks and challenges for organizations

1. Security risks: AI tools can expose sensitive or proprietary data.
2. Collaboration challenges: Different AI tools can hinder effective teamwork.
3. Increased costs: Individual licenses for various AI tools can add up quickly.
4. Lack of centralized control: BYOAI makes it harder to manage AI usage consistently.
5. Training challenges: Multiple different AI tools increases the cost and difficulty of training. 

What organizations can do to solve BYOAI

1. Consolidate usage to a single GenAI platform that provides users their choice of AI models and bots while centralizing purchasing, management, and administration.
2. Choose a consolidated platform that allows users to securely leverage existing organizational knowledge but does not use user data to train models. 
3. Choose a platform that provides discounts for volume purchases.
4. Choose a platform that provides ongoing and on demand training for AI.  
5. Choose a platform that is built around the collaborative use of Generative AI between teams of AI and teams of people.

There’s a single easy solution to do all of the above: BoodleBox.  

What happens when workers embrace GenAI?

According to the report: “AI makes their overwhelming workload more manageable (92%), boosts their creativity (92%), and helps them focus on the most important work (93%)—and it helps them feel more motivated (91%) and enjoy work more (91%).”

It’s not surprising then that “79% of leaders agree their company needs to adopt AI to stay competitive.”

To read the full report from Microsoft and LinkedIn: AI at Work Is Here. Now Comes the Hard Part (microsoft.com) 

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