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What is Human-AI Teaming in Three Levels of Complexity in Learning Environments?

Amy Corbitt — Mon, 11 Nov 2024 22:58:05 +0000

What is Human-AI Teaming in Three Levels of Complexity in Learning Environments? Amy Corbitt Mon, 11/11/2024 - 15:58

By: Ray Hao , Lucrezia Lucchi and Jamie Gorman

Ray Hao is a PhD student and Fulton Fellow in Human Systems Engineering at Arizona State University, studying under Dr. Jamie Gorman.

Lucrezia Lucchi is a Psychology PhD student in the Dynamics of Perception, Cognition, & Action Lab at Arizona State University. Lucrezia has a background in Exercise Physiology and Human Movement Sciences.

Professor Jamie Gorman is an expert in modeling and measuring coordination dynamics in human and human-machine teams in The Polytechnic School at Arizona State University.

In today’s rapidly evolving digital world, parents are faced with growing questions about how to provide the best education for their children. One increasingly important factor in education is Human-AI teaming where students collaborate with artificial intelligence (AI) technologies to enhance learning. But what does it actually mean for students and AI systems to collaborate as teams, and just how complex can this process be?

What is Human-AI Teaming in Learning Environments?

Human-AI teaming in learning environments refers to the collaboration efforts of humans (teachers, students) and AI systems. It aims at enhancing educational outcomes by combining the unique strengths of both. In these settings, AI systems do not replace humans; instead, they work side by side with teachers and students to support and improve the learning process. Human-AI teaming can vary in complexity, starting with basic AI assistance and evolving into more collaborative teamwork and community, each offering distinct opportunities to enhance learning.

Level 1: Basic AI Assistance – Personalized Learning

At the foundational level, AI helps students by scaffolding personalized learning experiences and guiding students through customized learning experiences. This approach focuses on individual optimization by helping students progress at their own pace. Many tools on this level offer feedback, hints, and explanations tailored to the student’s needs but without direct group collaboration in the learning process.

Example: Duolingo, for instance, provides an adaptive and interactive experience where users progress through language lessons tailored to their learning pace. It adjusts the difficulty of lessons based on user performance, offering targeted hints and explanations to address specific challenges. Similarly, Khan Academy personalizes learning in subjects like math and science, suggesting exercises that align with the learner's current understanding. The platform offers immediate feedback, allowing users to correct errors in real-time and supporting a structured, individualized learning journey.

Level 2: Collaborative AI Partners – Learning Together

At this level, AI not only tutors students but also encourages collaboration in the learning process. It works alongside students in group projects, providing real-time insights, asking guiding questions, and even learning from interactions. By encouraging critical thinking and teamwork, AI makes learning more interactive and engaging. The Institute for Student-AI Teaming (iSAT) envisions classrooms where AI and students collaborate on problem-solving tasks, helping each other through challenging concepts while developing critical thinking skills (D’Mello et al., 2024).

Example: Our AI partner CoBi (Community Builder) is a great example of this. Designed for classroom use, CoBi helps groups of learners improve their collaboration skills by focusing on how they interact and work together. Another example is Kahoot! Team Mode, which supports AI-powered analytics to adjust in real time according to the group's collective performance. This platform has become a popular tool among educators to enhance students' collaboration and teamwork skills.

Level 3: AI-Enhanced Communities – Knowledge Building

At this level of complexity, AI is deeply integrated into the classroom environment, working as both a facilitator and a teammate for collaborative learning. It assists teachers in managing class-wide discussions, offers insights into student participation, and highlights key moments of critical thinking or engagement, nurturing an inclusive learning community where every voice is valued (Langer-Osuna, 2017).

Example: iSAT’s AI partner JIA (Jigsaw Interactive Agent) is designed to enhance collaborative learning by supporting student interactions and promoting effective group dynamics through real-time prompts and interventions. JIA encourages students to actively listen, share ideas, and build on each other's contributions in jigsaw activities, fostering deeper engagement and understanding. IBM Watson Education Classroom also offers an additional approach to personalized learning by helping teachers monitor and manage content, providing insights into student participation and needs to support learning outcomes for the entire class.

Potential Challenges & Concerns

As technology continues to evolve, we can expect even greater collaboration between humans and AI to enhance the way students learn, making education more personalized and effective than ever before. Despite the potential benefits, such as tailored learning experiences, improved collaboration skills, and increased accessibility to educational support, there are also challenges and concerns associated with Human-AI teaming.

One major concern, according to Alrazaq and colleagues (2023), is the risk of over-relying on AI, which could hinder the development of critical thinking and creativity in both teachers and students if not used in a balanced way. For example, students may become accustomed to relying on AI for quick answers or guidance, which can impede their capacity for thorough research and independent insight formation, potentially diminishing critical faculties. Similarly, teachers might depend on AI gaining insights for student assessment and feedback, potentially bypassing deeper observation and reflection on individual learning needs. This reliance can deter students from developing skills that are crucial for academic and professional success (see the studies of Koos & Wachsmann, 2023, and Zhai and colleagues, 2024, respectively). Navigating the use and deployment of AI in education also poses significant challenges, as integrating AI systems for personalized learning and automated assessments can lead to inconsistencies when evaluating students' progress compared to traditional methods. Additionally, privacy concerns arise as AI systems collect and analyze student data in various ways, raising questions about how this data is used and protected. Lastly, there is the issue of equity. The National School Boards Association defines educational equity as “the intentional allocation of resources, instruction, and opportunities according to need, requiring that discriminatory practices, prejudices, and beliefs be identified and eradicated.” Not all students have equal access to technology, and if Human-AI teaming becomes central to education, it could widen the digital divide between those with access to high-quality AI tools and those without. Ensuring that these tools are appropriately integrated as school resources requires widespread education on their availability and growing relevance to academic curricula.

Why Human-AI Teaming Matters for Our Children

Incorporating AI into learning environments isn't just about optimizing test scores – it is about preparing students for the future. As AI continues to evolve, the ability to work alongside AI partners will be a crucial skill. Educational research highlights that interactive and collaborative approaches to learning are the most effective, supporting the goal of incorporating AI to help students adaptively solve real-world problems, rather than focusing solely on individual mastery of narrow topics, while also developing critical thinking and teamwork skills that are vital for success in today’s workforce (see NASEM, 2018, Fiore and colleagues, 2018, and D’Mello and colleagues, 2024, for more information). By teaming up with AI at multiple levels of complexity, students have an additional platform for learning to collaborate effectively, think critically, and creatively problem-solve. These collaborative experiences empower students to succeed in the classroom and beyond, equipping them with the skills they need to navigate an increasingly complex and technology-driven world.

AI has entered the mainstream in classrooms, and there are different visions for how AI should be used to educate students. One vision, according to Vee (2024), is to replace human teachers with bots that are subject matter experts, capable of teaching any subject. Another approach, which we embrace, is human-AI teaming, in which students and teachers team with AI to enable new concepts of learning. We feel this latter approach may better engage learners with each other and their teachers by supporting collaboration, rather than students learning to interact primarily with closed, AI-based systems that may lack the richness and creativity of human interaction.

References

Abd-Alrazaq, A., AlSaad, R., Alhuwail, D., Ahmed, A., Healy, P. M., Latifi, S., ... & Sheikh, J. (2023). Large language models in medical education: opportunities, challenges, and future directions. JMIR Medical Education, 9(1), e48291. doi:10.2196/48291

D'Mello, S. K., Biddy, Q., Breideband, T., Bush, J., Chang, M., Cortez, A., ... & Whitehill, J. (2024). From learning optimization to learner flourishing: Reimagining AI in Education at the Institute for Student‐AI Teaming (iSAT). AI Magazine, 45(1), 61-68. https://doi.org/10.1002/aaai.12158

Fiore, S. M., Graesser, A., & Greiff, S. (2018). Collaborative problem-solving education for the twenty-first-century workforce. Nature Human Behaviour, 2(6), 367-369. https://doi.org/10.1038/s41562-018-0363-y

Koos, S., & Wachsmann, S. (2023). Navigating the Impact of ChatGPT/GPT4 on Legal Academic Examinations: Challenges, Opportunities and Recommendations. Media Iuris, 6(2). https://doi.org/10.20473/mi.v6i2.45270

Langer-Osuna, J. M. (2017). Authority, identity, and collaborative mathematics. Journal for Research in Mathematics Education, 48(3), 237-247. https://doi.org/10.5951/jresematheduc.48.3.0237

National Academies of Sciences, Division of Behavioral, Social Sciences, Board on Science Education, Board on Behavioral, Sensory Sciences, ... & Practice of Learning. (2018). How people learn II: Learners, contexts, and cultures. National Academies Press.

National School Boards Association. (n.d.). Center for Public Education: Equity. Retrieved November 6, 2024, from https://www.nsba.org/Services/Center-for-Public-Education#:~:text=Equity,beliefs%20be%20identified%20and%20eradicated.

Vee, A. (2024). AI pioneers want bots to replace human teachers - here’s why that’s unlikely. The Conversation. Retrieved 10/10/2024 from https://theconversation.com/ai-pioneers-want-bots-to-replace-human-teachers-heres-why-thats-unlikely-235754.

Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7

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Considering Learning and Evidence of Impact in Evaluating the Potential of AI for Education

Amy Corbitt — Tue, 29 Oct 2024 16:10:09 +0000

Considering Learning and Evidence of Impact in Evaluating the Potential of AI for Education Amy Corbitt Tue, 10/29/2024 - 10:10

William Penuel

William R. Penuel is a professor of learning sciences and human development in the School of Education at the ��ƷSM��ӰƬ. His current research examines conditions needed to implement rigorous, responsive, and equitable teaching practices in STEM education. At iSAT, he is a Co-Principal Investigator and Co-Lead of Strand 3 - which focuses on inclusive co-design processes to empower stakeholders with diverse identities to envision, co-create, critique, and apply AI learning technologies for their schools and communities.

As school and district leaders, you are used to building planes while flying them. But the advent of AI—specifically Generative AI—in classrooms has caught many of us off guard and not sure what airspace we’ve entered. Generative AI is the technology behind popular tools like ChatGPT, as well as tools today that use AI to help teachers build lesson plans and assessments for use in their classrooms. It’s a specific kind of AI that learns from the data it’s been fed (such as text, video, or images) to create new content. If you’ve tried it out, you may be impressed both by its capabilities to simulate human interaction, as well as its limitations.

As an education leader, Generative AI presents many interrelated challenges to you, to teachers, to parents, and to students pertaining to safety, transparency, and ethics. In this blog post, we want to focus on two other central issues that Chief Academic Officers, district technology leaders, principals, and instructional coaches should keep in the foreground when evaluating the potential integration of AI into schools: learning and evidence of impact. Learning has to do with both our goals for learning and how we support them. Evidence of impact has to do with the power and limits of tools to achieve those learning goals. Good evidence also involves evidence of what’s required of teachers to implement tools well, to achieve benefits for students. Both these considerations are important in evaluating Generative AI and other tools, but often they live in the background of discussions about Generative AI.

Take the discussion of the potential of Generative AI for personalization and differentiation of learning. This is chief among the advantages that advocates of AI tout. The questions to consider are: What kinds of learning goals can Generative AI support? What do we know about the potential of Generative AI for supporting these goals?

Intelligent Tutors Help Personalize Individuals’ Mastery of Discrete Knowledge and Skills

There is more than 50 years of research on intelligent tutoring systems (ITSs) that we can draw on to give us a sense of what learning goals AI for personalization can support. ITSs are trained when their developers subdivide knowledge to be taught into smaller components—skills, abilities, and concepts—allowing ITSs to recommend tasks based on a student’s mastery level. There’s a large body of evidence of impact that suggests that for the kinds of problems ITSs are used to help students with, they do as least as well as human tutors do in supporting learning.

However, while AI excels at guiding students toward specific, well-defined learning goals (like solving a math problem), it struggles with more open-ended tasks where multiple solutions exist, or where collaboration and dialogue are essential. Further, it may limit deeper engagement and valuable experiences like productive struggle or peer collaboration. The evidence base applies only to well-designed ITSs, as well. Many of the Generative AI tools today can’t achieve the results of the best ITSs. While they are good at handling requests in everyday language, many of these tools still give inaccurate answers to math problems students encounter in schools.

This is not to say that Generative AI won’t become more capable of solving math problems or helping support critical thinking, teamwork, and real-world problem solving in the future, but there is not strong evidence of impact for achieving these learning goals. There is even less evidence related to what’s needed to prepare teachers to use these tools well. There’s reason to be skeptical, then, about claims that the current class of tools of Generative AI can support these goals.

AI Can Support Collaborative Problem Solving in Inquiry-Rich Environments

There’s an equally rich body of evidence of impact for a set of AI tools that support collaborative learning. For more than two decades, the field of computer-supported collaborative learning has created and tested different tools focused on fostering group awareness and giving students feedback on small groups’ cognitive and social dynamics. A review of these kinds of group awareness tools show improvements to students’ knowledge and skill, as well as group task performance and social interaction in collaborative learning. The relevance of these findings for K-12 schools, though, is not as clear, because many of these tools were designed for online environments in higher education.

Here’s where emerging research comes in – the kind designed to build evidence of impact grounded in a robust vision for teaching and learning. The Institute of Student AI-Teaming is developing AI partners—the Community Builder (CoBi) and the Jigsaw Interactive Agent (JIA)—that perform the key functions of group awareness tools. These tools are intended to be integrated with rich curricula that focus on collaborative problem solving in STEM. These tools do something very different from what Generative AI tools as currently used to plan instruction or support personalization do: they help students learn to collaborate more effectively and equitably. They support a different kind of learning, too, one that is focused on students figuring out ideas and solving problems together, using disciplinary practices from STEM that are targeted in today’s standards. And while we are still gathering evidence of impact, we already know that students are using some collaborative solving skills more when they are using an AI partner to support their learning. We aim to make these partners—and the instructional materials to teach about AI—available to schools for free in the coming year.

Questions to Ask About Learning and Impact

AI is here to stay, and as a leader, you know you have an obligation to approach how to use AI responsibly and ethically to achieve your vision for teaching and learning. No doubt, AI may now or in the future be useful for increasing efficiency in how teachers plan and how students develop discrete knowledge and skill. As vendors continue to rush to offer generative AI products to schools and districts, it’s important to ask three questions:

What kind of learning does this tool support?

What kind of preparation do teachers need to use the tool well?

What evidence of impact is there for the claims being made about Generative AI?

Integrating AI into classrooms is likely to lead to changes in how teachers teach and how students learn. Teachers will need support in learning how the AI works, and how to use AI tools to support teaching and learning that is consistent with what we know about how students learn. A generative AI chat bot doesn’t understand how people learn, no matter how skillful its interactions seem. That leaves it as your responsibility as a critical consumer of AI tools to ask tough questions of vendors about their ideas about teaching and learning and to demand they present evidence of bold claims about the power of AI.

Now is a moment when we are all particularly open and keen to learn about AI, and it is as imperative as ever to create opportunities where educators and leaders can learn together about the potential and limits of Generative AI and other tools that support learning goals for collaborative problem solving. We not only have to be “in the loop”: as decision makers about teaching and learning, we need to stay at the center, working at a pace that protects both our children and takes care of our visions for teaching and learning and that follows evidence more than hype.

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A High Level Overview of Building the iSAT MakeCode Activity Logging Platform

Amy Corbitt — Wed, 23 Oct 2024 20:53:07 +0000

A High Level Overview of Building the iSAT MakeCode Activity Logging Platform Amy Corbitt Wed, 10/23/2024 - 14:53

Sachin Rathod

Sachin Rathod is a Software Engineer working on full-stack development of iSAT’s AI Partners with the Institute-wide team. He is also a graduate student pursuing a master’s degree in computer science at ��ƷSM��ӰƬ. His interests and expertise are in Machine Learning, Distributed Systems, and Cloud Computing.

The iSAT MakeCode Activity Logging Platform provides a robust system for tracking and analyzing user coding activity in the Microsoft MakeCode environment. It is an expanded version of the Microsoft MakeCode blocks/JavaScript code editor for the micro:bit, designed to log and track user coding activity.

We leverage AWS services to ensure that every user coding action is logged efficiently and can be used for both real-time analysis and future learning. Whether users are looking to monitor coding sessions or integrate this data into machine learning models, this platform offers an extensible solution to track and store all the necessary data.

In this blog post, we provide the technical architecture for the iSAT MakeCode and how it has been implemented.

Figure 1. The iSAT MakeCode block editor.

The iSAT MakeCode Activity Logging Platform provides a deeper insight into how users interact with the micro:bit block-based editor, logging every code edit into a NoSQL database AWS DynamoDB for future analysis enabling both real-time monitoring and post-session review of coding activities.

The platform is built around two key components: the Extended Web-Based Micro:bit Block Editor and the Back-End Data Logging API Server. The web-based editor serves as the main interface, allowing users to interact with a blocks/JavaScript editor. We started with the open source Micro:bit Block Editor code base and enhanced it to track and log coding activities, including block additions, deletions, and code modifications.

The back-end server is responsible for logging each coding activity to a database, allowing developers and researchers to analyze user behavior and feed the data into machine learning pipelines. The server operates in the AWS cloud and receives logging messages sent by the user’s browser as they interact with the block code editor. Each edit version is logged to the database in MakeCode’s JavaScript format along with a timestamp and the action performed on the block (create, delete, modify), providing a transcript of the progression from the start to finish of their coding session. Each JavaScript version can be analyzed, and researchers can paste the JavaScript back into the block code editor to see a visual representation of the code blocks at the given time in the user’s coding session.

iSAT uses a variety of key technologies to support these functionalities including:

Microsoft MakeCode for Micro:bit: Used as the base platform for coding in blocks and JavaScript.
AWS DynamoDB: Stores all the coding activities.
AWS ECS (Elastic Container Service): Hosts both the frontend and backend services.
AWS Fargate: Provides the infrastructure for running containers and services.
Node.js: Used for backend server logic and communication with DynamoDB.

How It Works

The MakeCode Activity Logging Platform is built to be scalable and efficient, leveraging cloud-native technologies on AWS. The below points cover typical user actions and data flow within the system.

User Action Flow:
- When a user requests for iSAT MakeCode Activity Logging Platform (front-end application), an API Gateway (shown in the top left of Fig 2. Architecture diagram) manages and routes the request to application load balancer. The load balancer then routes the request to the next available AWS Fargate tasks (containers) that serve the front-end application.
- Upon successful response, a user logs into the MakeCode system and joins a study session by entering their Study ID and Session Code.
- The user proceeds to work on coding tasks within the MakeCode editor, usually provided as tutorials.
- All actions, such as block additions, deletions, and modifications, are sent across to the Back-End Data Logging API Server deployed on AWS ESC (shown in the top right of Fig 2. Architecture diagram). The backend-server logs these actions to AWS DynamoDB (shown in the bottom right of Fig 2. Architecture diagram) in real-time.
Logging System:
- Each code modification is stored in its MakeCode JavaScript format, meaning you can both analyze the logs and re-input them into the MakeCode editor to visualize the user’s coding process step by step.
- All events are timestamped, making it easy to track the coding session's progression.
Deployment:
- The frontend and backend services are deployed on AWS ECS clusters. AWS Fargate containers for both services are managed using ECS, ensuring high availability and ease of scaling.
- An API Gateway manages and routes incoming requests to the appropriate service, whether it’s a frontend action or a backend log storage.
- AWS Auto Scaling Groups and Application Load Balancers ensure that the system can handle varying loads, ensuring both scalability and reliability.

Figure 2. Architecture Diagram

Installation and Setup

To use this platform, developers need:

An AWS Cloud Account to set up and deploy the services.
GitHub to access the platform’s code and deploy it onto AWS ECS.
Basic knowledge of AWS services (like DynamoDB, and ECS, and Fargate) and the Microsoft MakeCode editor.

Please contact our team at Info.AI-Institute@Colorado.edu for more information and to schedule a consultation.

The iSAT MakeCode Activity Logging Platform provides a robust system for tracking and analyzing user coding activity in the Microsoft MakeCode environment. It is an expanded version of the Microsoft MakeCode blocks/JavaScript code editor for the micro:bit, designed to log and track user coding activity.

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Where Does the Data Go? A Behind-the-Scenes Look at iSAT’s Security Measures for Classroom Data Collection and Handling

Amy Corbitt — Fri, 18 Oct 2024 01:24:29 +0000

Where Does the Data Go? A Behind-the-Scenes Look at iSAT’s Security Measures for Classroom Data Collection and Handling Amy Corbitt Thu, 10/17/2024 - 19:24

By Charis Clevenger

With a Master's in Family and Human Development, Charis’s personal research interests include AI in education, relationship building, and learning through collaboration, equity in public schools, and viewing learning through the biopsychosocial model.

Do you ever wonder what happens to student data once the microphones and cameras are out of the classroom? With AI in education, there can be a lot of questions and concerns about how ��ƷSM��ӰƬ is protecting students’ information, whether it be their name, voice, image, or even the work they submit in class. It is challenging enough to navigate the school age years – worrying about how data remains secure shouldn’t be one of the contributing factors.

My name is Charis Clevenger, and I am the data manager for the Institute of Cognitive Sciences and iSAT. As a mother and former educator, the protection of vulnerable populations including our children is a critical motivating force in my role as data manager. Having been with iSAT since its founding (we are now in year 5), I make it a priority to ensure that we keep up to date with the latest best practices and safest measures for securing the data we collect.

iSAT, as a whole, is committed to following the Responsible Innovation Framework proposed by Stilgoe and colleagues (2013) where we protect the future from harm by emphasizing a stewardship of science and innovation in the present. Below are some ways how we apply this framework for our research policies on collecting data in classrooms.

Anonymizing personally identifying information at every stage

The first step after we collect data involves removing any information from the data that can identify a student participant. For this, we use study IDs instead of students’ real names. We also anonymize any information about their context, whether it’s who their teacher is, which school they attend, and what district they are in. Additional measures we take are:

Using untraceable identification numbers,
Blurring videos used for general analysis,
Transcribing speech to minimize the need for additional video use.

Ensuring raw data is secure once collected

Data is kept on secure servers that are password protected. Data collectors follow rigorous cyber security protocols and safeguards such as never “staying logged in” to any data networks.

Additionally, iSAT has put into place the careful curation of datasets based on specific needs from our in-house expert research teams. This happens only after the collected data has been rigorously checked and rechecked for any issue that could reveal identifying information. For example, suppose there is a school announcement made over the intercom during data collection and it may contain identifying information about the school; if this ends up being audible on the recording, we remove it. In doing so, our team ensures that collected data has to pass several levels of inspection and cleaning as well as move through various access control channels before it ever gets forwarded to research teams. And then we also track what data is being used and by whom. This minimizes the access to data that is not necessary to complete research by any given team.

In summary, it is imperative to update and refine security measures that protect the privacy of student participants. That is why iSAT has created a system that runs all collected data through various pre-processing and cleaning stages, limits access to data for research purposes only, and securely stores data for the lifetime of its use.

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The iSAT Blog in Year 5

Amy Corbitt — Mon, 07 Oct 2024 21:43:02 +0000

The iSAT Blog in Year 5 Amy Corbitt Mon, 10/07/2024 - 15:43

As we enter our fifth year as an Institute, we’re excited to expand the iSAT blog. One of our principal goals is to address the central challenge of how to promote deep conceptual learning via rich socio-collaborative learning experiences for all students. We are pursuing this challenge through the development of AI partners that are intended to provide real-time classroom support and to augment collaborative learning. We are going to use this space to look at different topics in the area of Artificial Intelligence in education (AIEd) and to provide a behind-the-scenes look to show how we conduct our work.

From October through April, our blog will feature around 25 posts covering a range of topics. These will include technical discussions such as “Multimodal Large Language Models: iSAT's work on building discrete multimodal language models to facilitate multiple speech processing tasks” and “Gesture Detection: Identifying key moments when a gesture occurs and determining what the gesture was” as well as broader topics in AI like “Where Does my Data Go? A discussion on security measures for participants’ data” and “AI to Support Collaboration vs Generative AI (ChatGPT)”.

These almost weekly posts will cater to different audiences including parents, school administrators, teachers, developers, students, researchers and policy makers. Each post will be authored by team members from our various research “strands.” These strands are integral to the development of the AI Partners, each focusing on a specific area of research. In addition to covering the different strands, many blog posts will feature cross-strand collaboration, showcasing how these different research areas intersect and come together at iSAT. The diverse range of topics will provide a comprehensive behind-the-scenes look at the work of our Institute.

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iSAT Curriculum Series: Forward to the Future: The Self-Driving Car Curriculum Unit for Middle School STEM Classrooms

Anonymous — Tue, 16 Jul 2024 21:05:34 +0000

iSAT Curriculum Series: Forward to the Future: The Self-Driving Car Curriculum Unit for Middle School STEM Classrooms Anonymous (not verified) Tue, 07/16/2024 - 15:05

By Jeff Bush

Jeff Bush is an Assistant Research Professor at the Institute of Cognitive Science at ��ƷSM��ӰƬ. He is also a theme lead at iSAT. His research focuses on the intersection of technology, STEM teacher learning and professional development with sub-topics of mathematics education, computational thinking, physical computing, formative assessment, complex instruction, Artificial Intelligence, user experience research, compassion, and equity.

In today's AI obsessed technological landscape, the Self-Driving Car (SDC) Unit puts students in the fast lane for learning innovative and responsible AI skills. Aimed at giving students technical proficiency, ethical judgment skills and hands-on collaborative skills, this unit dives deep into the complexities of programming autonomous vehicles while integrating cutting-edge AI-embedded technologies.

What is the Self-Driving Car Unit and How Does it Work?

The Self-Driving Car Unit immerses students in the exciting world of autonomous vehicles, putting them in the driver’s seat with an interdisciplinary approach. It begins with an engaging launch phase, featuring videos and discussions that highlight the real-world challenges and ethical dilemmas associated with self-driving cars. Students explore scenarios where a self-driving car must make split-second decisions, such as navigating around obstacles or deciding when to hand control over to a human operator.

As the unit progresses over 12-15 classes (typically spanning 3-4 weeks), students steer into the fundamental concepts of AI and robotics. They learn about data collection, training classifiers, neural networks, and the ethical implications of AI decision-making. Practical sessions involve programming their own miniature SDCs using platforms like the BitCar, where they implement features such as line-following, obstacle avoidance, and mode switching between autonomous and human-controlled operation.

How is This Unit Helping Kids in Classrooms - Specifically with Collaboration?

Central to the success of the Self-Driving Car Unit is its emphasis on collaboration. Students are organized into groups, each specializing in different aspects of SDC functionality like line following or object avoidance. This structure encourages teamwork as students share knowledge, brainstorm solutions, and troubleshoot challenges collectively. They then come together into a mixed group with one expert from each group; students teach about their feature to others and learn about the other two features from their peers. This peer-to-peer teaching not only reinforces understanding but also promotes effective communication and collaboration skills essential for future careers in STEM fields.

How is This Unit Tied into Our AI Partner CoBi?

Our AI partner, CoBi, plays a crucial role in enhancing the learning experience. Throughout the unit, CoBi provides support for collaboration and meta-reflection on how to best work in groups. Students collaborate in small groups and then CoBi gives them examples of how they did a good job upholding their co-negotiated class community agreements. This helps students develop these critical collaboration skills and be more adept at applying those skills in new contexts. The positive reinforcement and noticings help prevent a surveillance relationship and keep pushing students’ thinking by using actual examples from their class.

The integration of an AI partner such as CoBi aligns seamlessly with educational standards such as AI4K12 and CSTA, emphasizing computational thinking, problem-solving, and the societal implications of technology. This holistic approach prepares students not only to understand the mechanics of self-driving cars but also to critically analyze and contribute to the ongoing development of AI technologies.

In conclusion, the Self-Driving Car Unit represents a paradigm shift in STEM education, leveraging hands-on learning and AI-driven support to cultivate a new generation of innovators and problem-solvers. By exploring the three way intersection of robotics, AI, and ethics, students not only gain technical skills but also develop the collaboration and critical thinking abilities necessary to put the pedal to the metal in a technology-driven world. As we continue to build more AI-powered curricula, this unit stands as a testament to the power of integrating cutting-edge technology into educational curricula.

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iSAT Curriculum Series: Games Unit

Anonymous — Wed, 26 Jun 2024 16:14:59 +0000

iSAT Curriculum Series: Games Unit Anonymous (not verified) Wed, 06/26/2024 - 10:14

By Monica Ko

Monica is an Assistant Research Professor at the Institute of Cognitive Science at ��ƷSM��ӰƬ. At iSAT, she investigates how inclusive co-design processes can empower teachers and students with diverse identities to better understand how AI learning technologies can be used for good in their schools and communities.

At iSAT, part of who we are and what we do is dedicated to creating engaging STEM and Science curriculum units that highlight the power of collaborative learning. We then incorporate our AI Partners to enrich these units further. We are excited to present a series of blog posts showcasing these curriculum units, starting with a spotlight on the Moderation unit.

What is the AI Moderation unit?

The Moderation unit is a 2-3 week instructional unit that invites middle school students to figure out sources of bias and racism that emerge within a video game and gaming community, and envision how humans and AI might be used to imagine new kinds of gaming communities. The unit focuses on Minecraft and opens with a story about a teenager who has vastly different experiences playing Minecraft on two different servers. After reading the teenager’s story, students generate questions they have about AI, Game design, servers, and about moderation. These questions are then organized on a question board that drives the direction of the unit. In the following lessons, students investigate the kinds of moderation rules that exist across different games and gaming communities; they read about how humans and AI systems are used to moderate behavior. The curriculum encourages students to think about moderation strategies that not only ban “bad” behavior, but also those that recognize positive behaviors. These experiences lead students to think about the ideologies that underlie these moderation systems, as well as the limits and affordances of AI, Humans + AI, or Humans-only approaches to moderation.

Students also learn about sentiment analysis, a natural language processing (NLP) technique that is used to moderate a player’s affective state during gameplay. To better understand what this actually involves computationally, students build their own sentiment bots and discuss how their lived experiences and the volume of training data influence the models’ predictive power. Finally, students apply these ideas to their own gameplay by creating rules for moderation and enacting them during a multiplayer game of Minecraft. From this experience, they realize how challenging the work of moderation is and the importance of both context and interpretability in making these decisions – and how AI and humans can work together to create more just moderation systems.

How it’s Helping Students in the Classroom

Students are really excited to bring in their expertise with video games and online communities into this unit! It is a hallmark feature of the unit and reflects iSAT’s design principle of both soliciting and building from students’ everyday knowledge. When students are engaging with the Moderation unit, they bring in their knowledge about servers, positive and negative experiences of gameplay, and the importance of context and relationships in deciding whether players are joking or being insulting during gameplay. We know that students have some ideas about AI and its role in online communities, and this unit deepens this knowledge by getting students to understand how moderation works, how it influences human behavior, and what is important to pay attention to when developing these systems if our goal is to create more just futures.

One of the most powerful supports for this kind of learning is our AI Partner, CoBi! Short for Community Builder, students are introduced to CoBi at the beginning of the lesson, and CoBi is used multiple times to monitor how students collaborate during the unit. In essence, students are not only learning about moderation, but they are also experiencing AI moderation at the same time! This “meta” experience allows them to more deeply understand how AI systems work, their potential fallibility at various points of their inception, and how humans can become the co-constructors (and not merely users) of more just moderation systems.

Furthering Our Mission of Developing AI partners

The enactment of the Moderation unit addresses two of iSAT’s goals. First, it helps us better understand what and how students can learn about AI systems at the upper middle and lower school grade levels. There are currently no standards for AI learning in US classrooms, and this is one way for us to gather empirical data about what students are ready to explore on this topic. Second, the integration of CoBi into the Moderation unit directly supports the generalizability of the models that power CoBi’s analytical pipeline. Providing CoBi with training data across all of our Strand 3 units ensures that its impact is not just specific to one curricular unit, but that it can be used more widely across classrooms enacting different content. Third, having students simultaneously investigating and experiencing moderation makes the learning come alive in classrooms! Embedding CoBi within the AI Moderation unit creates unique opportunities for students to analyze and critique where CoBi is doing well and where it needs additional support and training data. This positions students as partners who provide critical feedback on the development and refinement of our AI Partners.

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Responsible Innovation in AI: Fostering Ethical and Sustainable Progress

Anonymous — Wed, 17 Apr 2024 18:31:04 +0000

Responsible Innovation in AI: Fostering Ethical and Sustainable Progress Anonymous (not verified) Wed, 04/17/2024 - 12:31

Responsible Innovation in AI: Fostering Ethical and Sustainable Progress

For a bit more than a year–effectively since the launch of Open AI’s chatbot ChatGPT at the end of November 2022–Artificial Intelligence (AI) has been the talk of the town. It has incredible potential to revolutionize industries, improve our daily lives, and address complex societal challenges. However, as AI becomes increasingly pervasive, the need for responsible innovation is more critical than ever. AI partners, specifically in education, offer the potential to transform the way students learn, how teachers instruct, and how educational institutions operate. But the adoption of AI partners in education must be guided by ethical considerations, equity concerns, and a commitment to ensuring that students’ concerns are addressed and interests upheld.

When developing educational AI tools, we must prioritize privacy and data security, taking the utmost care that student information is protected from misuse or breaches. Transparent data practices and clear consent processes for data collection are essential components of responsible AI. From the inception of iSAT, we adopted the framework of responsible innovation, as described by Stilgoe, J., R. Owen, and P. Macnaghten in Developing a framework for responsible innovation, which means “taking care of the future through collective stewardship of science and innovation in the present.” This framework was specifically developed to guide scientific and technical research in sensitive areas, such as genetics and geoengineering. It reflects the kinds of questions the public asks of scientists and expects scientists to ask of their own work; for example: Is this safe? Can I trust this information (is it reliable and credible)? How does this affect me/my community? It is particularly appropriate in the area of AI, where there are significant ethical concerns about anticipated and actual harms of AI technology, as well as the unequal distribution of harms in society such as in the criminal justice system, education inequalities, and the digital divide - just to name a few. This framework is reflected in our methods, our commitment to inclusive processes involving diverse stakeholders, and our ethics frameworks and training for Institute members.

By focusing on responsible innovation when it comes to AI in education, we hope to achieve a broader impact of our Institute - leading the nation towards a future where all students— especially those whose identities are underrepresented in STEM—routinely engage in rich and rewarding collaborative learning by working in teams composed of diverse students and AI partners. In this envisioned future, STEM classrooms become strong knowledge-building communities where student-AI teams engage in critical thinking and collaborative problem-solving as they investigate (local) scientific phenomena, solve real-world problems, or develop solutions for all kinds of design challenges.

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The Importance of Teacher Collaboration When Developing AI Partners for Education

Anonymous — Mon, 18 Mar 2024 15:56:40 +0000

The Importance of Teacher Collaboration When Developing AI Partners for Education Anonymous (not verified) Mon, 03/18/2024 - 09:56

The Importance of Teacher Collaboration When Developing AI Partners for Education

by Rachel Lieber - former teacher and research professional for iSAT for the Insitute's first 3 years.

Teachers are the ones in the classroom. They have a deep and lived understanding of their students, community, and school culture. Not only do they know what it takes for students to thrive, they know the challenges and obstacles of introducing novel ideas, whether it’s a new book or a piece of technology. Teachers also have an immediate understanding of their students’ diverse aspirations, interests, and needs. In other words, when it comes to questions of usability and accessibility of our AI partners, a teacher’s critical feedback is essential.

Over the course of our extensive collaborations with teachers, we have learned a great many things about the responsible integration of AI partners in classrooms. For example, we have come to learn that teachers are not concerned about technology taking their jobs. Educators understand that the complexities of their jobs cannot and should not be transposed into an algorithm. Their work is so much more than relaying information: they create community and develop confident learners and critical thinkers.

Teachers themselves are lifelong learners and eager to try technology that can make their lives easier or that enhances learning for their students. However, they can sometimes be bombarded by new technologies and new ways of doing things that are often not helpful. This has added a healthy level of skepticism when it comes to incorporating new technologies into their pedagogies, and if teachers are not enthused about technology coming into their classrooms, they are less likely to use it. Therefore, it is essential to include teachers early in the development process so that we get to hear their hopes, dreams, and concerns from the very beginning and develop technology that can provide meaningful solutions to these very real challenges.

One of the most effective things we have done at iSAT is to actively seek out criticism and look at all the potential outcomes, both positive and negative, of any given technology’s purpose in the classroom. Our technologies are not developed in a bubble; instead, our technologies are developed iteratively for use in classrooms and with teachers (and students) being integral parts of the co-design and evaluation process from the very beginning. When teachers share their ideas and knowledge surrounding how to make AI-enabled technologies fun and engaging and really help us think about how this technology is going to enhance their work and not just become one more thing they have to manage in the classroom, it keeps us focused on the question “How can our AI partners support teachers and students in a way beyond what the teacher is already doing?” For example, a teacher wants to know when a group is struggling and when they might need some additional support, or when a student is not actively collaborating and participating with others in the group. Teachers know they cannot be everywhere all at once so getting this type of information is something they simply cannot do on their own.

Our technologies need to be nimble enough so that teachers can adapt them to different circumstances. For example, teachers know the second a lesson starts tanking, but seasoned teachers also know how to pivot and course-correct. Their toolbox is filled with tools for engaging and motivating students to learn and work together. Humans are dynamic and K-12 students can be particularly unpredictable. These teachers work every day to reach and teach every student who walks into their classrooms; they know the value of collaboration in the classroom and how it can lead to higher-level thinking, increased self esteem, responsibility, and leadership skills. That knowledge, combined with their experience in engaging and motivating students daily, is invaluable for institutes such as iSAT that are engaged in developing educational support technologies specifically for the classroom.

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Value of K-12 Education / Higher Education Collaboration in Advancing Public Education

Anonymous — Sat, 10 Feb 2024 00:08:01 +0000

Value of K-12 Education / Higher Education Collaboration in Advancing Public Education Anonymous (not verified) Fri, 02/09/2024 - 17:08

Value of K-12 Education / Higher Education Collaboration in Advancing Public Education

By Axel Reitzig

Bio: Axel is currently the Executive Director of Innovation at the Innovation Center of the St. Vrain Valley School District. He is focused on developing innovative, dynamic and high-quality programming, both at the Innovation Center as well as across SVVSD.

There is one team-building activity that I have used for many years, both with students as well as with adults. It’s called Minefield and works like this: participants partner up and one gets a blindfold; the other is the guide who endeavors to bring their partner across the ‘minefield’ (a 20’x20’ space with boundaries on each side and ‘mines’, e.g. pieces of paper, distributed throughout). The goal? Bring the blindfolded partner across the minefield without, well, blowing him/her up!

There are different rules that can be imposed on the partners: for example, use only verbal commands or only taps on the shoulder. It’s a fun activity that fosters collaboration and innovative communication and definitely builds a sense of team. But here’s the real point of the exercise: in the activity, as in life, we often are working with others who either ‘see’ much more than us or, conversely, ‘see’ much less. This is figurative, of course, and certainly dependent on context. For example, a mentor has more insights into how certain things can or should be done than a learner. Figuratively speaking, the mentee is blindfolded and the mentor gives guidance through all the pitfalls that a new team member probably will encounter. Of course, in a different context, the mentee might well be the one who understands the minefield and can guide others through it.

This idea - that negotiating progress often requires collaboration between people with different levels of experience and understanding - applies to organizations as well as to people and teams. Consider the field of education. This field includes an extremely broad, diverse range of needs, goals, and experiences. Depending on one’s context, the minefield of challenges can look very different and require very different approaches to managing and getting through.

As a long-time PK-12 public educator, I have developed a lot of expertise in wending my way through a variety of different ‘mines’ as well as opportunities specific to my context. As it turns out, my wife is a long-time higher education educator. While there is certainly overlap between our two experiences, there are also clear differences. We have had many good discussions over the years comparing and contrasting our experiences!

So this gets me finally to the point of this blog post. Namely, what is the value of collaboration between PK-12 and higher education? Lots! Above all, we share the same fundamental vision, which is to develop capable, confident individuals ready to succeed in a highly diverse, complex world.

Because of our different contexts, however, how we achieve this vision may look different. And if we are not communicating and collaborating, then our efforts might well end up working against one another. Through my conversations over many years, not only with my wife but with my higher education colleagues and collaborators, I have a much better understanding and appreciation of the challenges and opportunities awaiting our students once they have graduated. Conversely, I hope, these folks in higher education better grasp the PK-12 landscape. And just as in Minefield, allowing one another to take the lead when it makes sense is mutually beneficial and greatly improves our chances of success.

This kind of vertical alignment and collaboration is productive for many other reasons. But, for me, one of the most impactful things is that I know I can rely on my higher education teammates for their expertise and guidance when it comes to things mostly outside of the scope of PK-12. In return, I hope that I have helped guide them through experiences in the PK-12 space.

This is the 4th year of our collaboration with iSAT and ��ƷSM��ӰƬ. The scope of this work has grown and changed and brought great value to our teachers, students and community. We couldn’t have done this without relying on the expertise, vision and guidance of so many members of the iSAT team! Thank you for this - we look forward to more successes and opportunities over the coming years!

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