Human -> AI -> Human: A Simple Approach to Using A.I. For Learning
There is an intense video making its way across the internet and social media this week.
A professor confronts one of his students about using AI to cheat. He apparently assigned five different videos (10 minutes each in length) to watch and respond to (100 words).
The student in question, turned in the assignment in four minutes. The professor, obviously upset, gets into a contentious back-and-forth as the student shows the AI tool they are using to watch the video, provide notes, and ultimately answer any questions.
The student reiterates over and over that they are not cheating, they are using the technology to help them learn.
The professor disagrees, and it seems like this is not the first time it is an issue. The student is subsequently kicked out of class.
I’m not sure either of the people in the video gave their consent in the filming, so I won’t share it out here.
But, this story is playing itself out in classrooms and organizations all around the world.
Interestingly enough, the comments are split. A good group of folks agree with the professor, and another big group see nothing wrong with what the students is doing and how they are using the technology.
I’ll often share different scenarios like this when I’m working with teachers and school leaders. It’s a great activity for the folks in the room to discuss, see different viewpoints, and talk about the gray-area that exists with using artificial intelligence for teaching, learning, and creating.
Some of the questions I would ask a group after discussing this scenario:
Did the student complete the assignment? Yes, the 100 word response was submitted.
Could the AI tool complete the assignment without the student prompting it? This seems unlikely.
Was the assignment completed all by the student, without help from AI? No.
Did the student go through each step of the assignment as the professor hoped for? Definitely, not.
Was anything learned during this assignment? Well, it’s complicated to answer without knowing many more details.
What was the purpose of the assignment? What was the context of the AI use?
The questions, and debate, could go on.
Start With Some Parameters
The first approach to take, is to make sure there are parameters for any assignment, activity, or assessment.
I’m assuming, given the professor’s reaction, that AI was not allowed to be used in this circumstance.
If we are looking at a simple parameters for a classroom like the Traffic Light Protocol, this assignment would fall under the RED category:
Via GVSD
Red Light: NO Artificial Intelligence use allowed on this assignment/activity/project.
Yellow Light: Get Permission to Use Artificial Intelligence on this assignment/activity/project.
Green Light: Encouraged to Use Artificial Intelligence tools on this assignment/activity/project, but let’s discuss how you plan on using it.
In the story shared above, if the Professor/Teacher had made it clear it was a Red assignment, then the AI use would violate that policy, and there shouldn’t be a back and forth conversation.
However, if AI use is always banned, then we start to see what is happening all around the country — students either cheating/plagairizing, or having complete disregard for these “all or nothing” policies put in place.
If students have access to technology, and live in a world that uses technology for all kinds of learning, creating, and working purposes—it makes it difficult to always deny the use of artificial intelligence.
Let’s say, for sake of an example, that the story above was changed a bit because the professor said the Assignment was Green or Yellow.
How would things change? What use would be appropriate for learning?
Human -> AI -> Human
One of my favorite guiding documents around AI use for teaching and learning, comes from OSPI in Washington.
Their approach is simple, and starts with a humane look at AI in education. Here’s how they describe the Human -> AI -> Human approach in their Human-Centered AI Guidance for K–12 Public Schools:
In K–12 education, uses of AI should always start with human inquiry and always end with human reflection, human insight, and human empowerment. This model, abbreviated as “Human AI Human” or “H AI H” throughout this guidance, offers pathways for educators, school district administrators, and students to engage with AI responsibly, ethically, and safely.
Apply this approach to the story above. The student could use their own human inquiry to find out more about the topic, use it to search for additional videos to go deeper on the subject, and then write their response. AI could help in many ways throughout this process, which would be followed by reflection on what they learned, how AI helped them process their learning, and what they may do different next time (or how to keep going down this learning path).
Here are a few examples from Perplexity sharing how students and teachers can use this approach in teaching and learning:
Language Learning
Human Inquiry: A high school Spanish teacher identifies that students struggle with verb conjugations and wants to provide more personalized practice.
AI Assistance: The teacher uses an AI-powered language learning platform that adapts to each student's proficiency level, generating customized exercises and providing instant feedback on verb conjugations.
Human Empowerment: Students review their AI-generated feedback with the teacher, discussing their progress and developing strategies to improve their language skills. The teacher uses the AI-generated data to inform future lesson plans and provide targeted support.
Writing Enhancement
Human Inquiry: A middle school English teacher wants to help students improve their writing skills and expand their vocabulary.
AI Assistance: Students use an AI writing tool like Grammarly to check their essays for grammar, style, and vocabulary suggestions.
Human Empowerment: The teacher guides students in critically evaluating the AI suggestions, discussing why certain changes were recommended, and deciding which to implement. Students reflect on how the AI feedback has improved their writing and identify areas for further development.
STEAM Education
Human Inquiry: A science teacher wants to introduce complex concepts like cause and effect, sequences, and patterns to elementary students.
AI Assistance: The teacher incorporates AI-powered interactive games and activities that help children visualize and understand these concepts.
Human Empowerment: After engaging with the AI-powered activities, students discuss their observations and insights with classmates and the teacher. They reflect on how the interactive experiences enhanced their understanding and apply the learned concepts to real-world scenarios.
Interactive Storytelling
Human Inquiry: A first-grade teacher wants to adapt story time to be engaging and interactive for young students.
AI Assistance: The teacher uses an AI-powered storytelling app that generates personalized stories based on students' interests and reading levels. The app incorporates interactive elements like voice recognition for students to participate in the story.
Human Empowerment: After the AI-assisted storytelling session, the teacher leads a discussion about the story, encouraging students to share their favorite parts and relate the story to their own experiences. This fosters language development and critical thinking skills.
Early Math Skills
Human Inquiry: A kindergarten teacher aims to help students develop basic numeracy skills in a fun, engaging way.
AI Assistance: The teacher incorporates an AI-driven math game like DreamBox that adapts to each student's skill level, providing personalized exercises and visual representations of math concepts.
Human Empowerment: The teacher reviews the AI-generated progress reports for each student, using this information to form small groups for targeted instruction. Students reflect on their learning by drawing pictures of the math concepts they've learned and sharing them with classmates.
Science Exploration
Human Inquiry: A third-grade science teacher wants to make complex concepts like the water cycle more accessible and interactive for young learners.
AI Assistance: The teacher uses an AI-powered virtual reality (VR) simulation that allows students to explore the water cycle in an immersive environment. The AI adapts the complexity of the simulation based on student interactions and understanding.
Human Empowerment: After the VR experience, students work in groups to create physical models of the water cycle, applying what they learned in the simulation. The teacher guides discussions about real-world applications of the water cycle, encouraging students to make connections between the virtual and physical worlds.
A final thought…
It is up to all of us in the teaching and learning space to navigate these new AI scenarios with a focus on keeping a very human experience (learning) as human as possible.
