Laura K Spencer, Ed.D.

Tag: Artificial Intelligence

  • The AI Revolution: A Wake-Up Call for Real Learning

    The AI Revolution: A Wake-Up Call for Real Learning


    The recent buzz around AI in education, exemplified by Elon Musk’s assertion that AI-assisted learning can already outperform human teachers, has sparked important conversations. However, I believe we’re focusing on the wrong question.

    We’re asking if AI will replace teachers, but we should be asking: is AI already replacing students in their own learning process?
    This question was recently raised on LinkedIn by Elena Beretta, who shared her observations of students leveraging large language models (LLMs) for everything from writing essays and solving homework to debugging code and even drafting theses. The driving force behind this widespread adoption? Increased productivity, she asserted. Students are drawn to the efficiency AI offers, allowing them to complete assignments in a fraction of the time. As Beretta points out, this isn’t necessarily about cheating – universities are addressing that – but about a fundamental shift in how students perceive learning.
    Beretta’s insights highlight a crucial trifecta of concerns: the shifting definition of learning, the delegation of “worthwhile” knowledge to AI, and the increasingly difficult role of educators. When productivity becomes the primary goal, the process of learning is devalued. If AI can instantly generate answers, what incentive do students have to grapple with critical thinking, problem-solving, and the development of structured arguments – skills that only improve through dedicated practice? This leads to AI effectively dictating what is “worth” learning, as students bypass the struggle inherent in developing these crucial skills. Consequently, educators are finding themselves in an exhausting loop, becoming less teachers and more AI-police and content verifiers. This begs the question: how can we equip students with the skills they truly need when AI makes it so easy to circumvent the learning process?
    I believe this situation underscores a pre-existing and deeply rooted problem in our educational system: the transactional view of schooling. For too long, students have been conditioned to see education as a series of tasks, points, and high-stakes tests, prioritizing metrics and data over genuine intellectual growth and the joy of learning. This transactional approach has already diminished the value of deep learning, and the advent of AI only amplifies this crisis. The “hustle” mentality, focused on efficiency and output, has become even more entrenched.
    If we don’t address this fundamental issue, we risk losing any hope of real learning taking place in schools. We need a paradigm shift, moving away from a system obsessed with productivity and embracing a performance-based model that prioritizes meaningful topics and the cultivation of essential skills. What matters most is fostering critical thinking, creativity, and a genuine love of knowledge – qualities that cannot be replicated by AI.
    Perhaps the disruption caused by AI can serve as a much-needed wake-up call. It’s time to fundamentally rethink our approach to education and ensure that learning isn’t just about completing tasks quickly, but about developing skills that are truly valuable and relevant for the future. This reality check could be precisely what we need to redefine learning for the better, shifting our focus from mere efficiency to the cultivation of human potential.

  • Learning Through Technology: AI in Education

    Learning Through Technology: AI in Education

    I recently had the privilege of being a guest on the Learning Through Technology podcast, where I engaged in a fascinating discussion about the legal and ethical implications of AI in education. Guesting with me was Gretchen Shipley from F3 Law, whose expertise in education law brought valuable insights to our conversation.

    As educators, administrators, and members of the education community, we’re all navigating the rapidly evolving landscape of AI technology in our classrooms and institutions. During the podcast, we explored critical questions about how to harness AI’s potential while ensuring we maintain ethical standards and comply with legal requirements.

    Some of the key topics we covered include:

    • The current state of AI adoption in educational settings
    • Legal considerations when implementing AI tools in the classroom
    • Ethical frameworks for decision-making around AI use
    • Practical guidelines for educators and administrators
    • The importance of maintaining academic integrity while embracing innovation

    I believe this conversation comes at a crucial time as more schools and districts are developing their AI policies and guidelines. Whether you’re an educator already using AI tools, an administrator crafting policy, or simply interested in the future of education, I encourage you to listen to the episode and share your thoughts.

    You can find the episode on the Learning Through Technology podcast platform. After listening, I’d love to hear your perspectives:

    • What has been your experience with AI?
    • What challenges have you encountered?
    • What opportunities do you see for the future?

    Let’s continue this important conversation in the comments below. Your insights and experiences can help shape how we collectively approach AI, both in education and the workforce.

    Check out the podcast episide:

    Fame Host
    Spotify
    Apple podcast

    If you found this discussion valuable, please share it with your colleagues and professional network. The more voices we have in this conversation, the better equipped we’ll be to shape the future of education.

  • Efficiency vs. Elimination: Rethinking AI Automation

    Efficiency vs. Elimination: Rethinking AI Automation

    Artificial intelligence (AI) is rapidly transforming our world, promising a future of streamlined workflows and maximized productivity. But in our rush to leverage AI’s power, are we focusing on the right outcomes? Is AI truly making us more efficient, or are we simply automating tasks that perhaps shouldn’t exist in the first place?

    Efficiency vs. Automation: A Key Distinction

    Efficiency is about doing things better, optimizing processes to achieve more with less. Automation, on the other hand, is about replacing human effort entirely. While automation can contribute to efficiency, it may not always be the preferred approach. Why?

    • AI can augment human strengths, not replace them. Tasks requiring creativity, empathy, and critical thinking still benefit from human input. AI can analyze data, identify patterns, and automate repetitive steps, but it can’t tell us the hidden story behind the data and patterns.
    • Not all tasks deserve automation. Some tasks may be inherently inefficient, and automating them simply perpetuates a broken system.

    Using AI to Ask the Right Questions

    Instead of simply automating existing processes, AI can help us ask better questions about the processes we are looking to streamline.

    • Is this task truly necessary? Could AI help us streamline processes or even eliminate unnecessary steps altogether?
    • Can AI augment human capabilities? How can AI assist us in making better decisions or perform tasks more effectively?
    • How can we ensure responsible AI implementation? Clear guidelines and human oversight are essential to mitigate bias and ensure ethical use.

    The Future of Work: A Human-AI Partnership

    There’s a lot of fear about the future of work, and whether or not the dystopian Terminator and iRobot movie society will come to be. I truly believe that it isn’t about humans vs. machines. By leveraging AI for true efficiency, we create opportunities to focus on the high-value tasks while AI handles the mundane. This not only increases productivity but also fosters a more engaging and fulfilling work environment.

  • Where are the Robot Teachers?

    Where are the Robot Teachers?

    Last night I was invited to speak to a class of preservice teachers about the role of IT in education. It’s a hard topic to address since it’s so vast and all-encompassing. Do I talk about servers and switches or how to placate grumpy IT Techs (haha) or share the nuances of configuring an MDM? I wasn’t sure so I went in empty-handed and ended up tackling all of those topics and more.

    In fact, one of the questions was about the future of technology in education and where I saw it heading. I brought up VR, AR, AI, etc but I shared one caveat – none of those technologies will make an impact without a teacher. I think (and hope) that, for many, COVID and learning from home has shown that teaching is much more than following a pacing guide or putting students on an intervention computer software for 30 minutes a day, every day. It’s both an art and a science.

    And as I reflected on that, I dug out a book I had read on Artificial Intelligence last year and laughed at all the connections between AI and teaching.

    You Look Like a Thing…

    In You Look Like a Thing and I Love You, Janelle Shane explains how Artificial Intelligence (AI) can sometimes be a terrible way to solve a problem. Honestly, they just aren’t as smart as we’ve been duped to believe.

    In fact, most of the issues engineers and researchers have been having with AI are probably issues you’ve confronted at some point in your teaching career.

    AI is Dumb

    I don’t mean the concept. I mean the actual computers running it. It’s not their fault. They just lack the capacity to perform a multitude of complex tasks at one time. Some work-arounds have resulted in numerous computers being strung together, each performing one part of a multi-part scenario (kind of like student project groups). But still, at their core, there’s some serious limitation.

    Consider how long it took you to learn to ride a bicycle. I’m sure you learned in less than the hundred crashes the robot had, and even then, it could only go a few meters without falling, and thousands more crashes before riding for a few tens of meters!

    Most of this is because computers can’t remember much – their brainpower is exerted on the immediate task, and so there’s not much ability to plan ahead and make generalizations.

    There are many instances in the book in which AI was terrible at solving a problem, and the reasons fell into a few categories.

    Too broad a problem

    In 2019, researchers from Nvidia trained an AI to generate images of human faces. It did pretty well, except for things like earrings not matching or bizarre backgrounds. But when asked to learn about cats, it got it all wrong, producing images with extra limbs, eyes, and distorted faces.

    When the AI trained on human faces, they were all forward-facing. But the cats were seen in all sorts of positions (as cats are prone to be) and so the AI couldn’t distinguish what exactly made up a cat face. Check out ThisCatDoesNotExist for creepy examples.

    We’ve seen it happen in our classrooms. We introduce an algorithm in math and all of a sudden, students are using it for everything, even when it makes no sense. Or we tell students that an essay hook can be to start with a question and then every single paper starts with a question until the next hook is introduced.

    Not enough data for it to figure out whats going on

    Most AI learn by example. If you give the machine enough examples of something, it learns the patterns and begins to imitate them. In one AI experiment, a machine was given different ice cream flavor names and told to create its own.

    Unfortunately, the machine doesn’t know what ice cream is, or even English, or how flavors work. it only knows how to translate each letter, space, and punctuation into a number and then keep analyzing those numbers for patterns. The result? Flavors like Bourbon Oil and Roasted Beet Pecans and Milky Ginger Chocolate Peppercorn.

    Textbooks are notorious for not giving enough data. How can the American Revolution be condensed into one chapter? Ask any textbook publisher and they’ll show you!

    Accidentally gave it confusing or non-needed data

    When I learned about the Essential Elements of Instruction, which is based on Madeline Hunter’s research, one of the elements was Teach to the Objective. I thought, “well that’s easy. Just teach the lesson” but it turned out to be much more complex than I realized.

    For example, if the objective is for students will list two major reasons for the Civil War, then teaching about how the economics of slavery and political control of that system was central to the conflict makes sense. However, if I tell the story about my trip to a plantation in Atlanta and how depressing it was to see the slave quarters, I’ve now begun a non-congruent conversation that may lead to confusion as to what the objective is, and what students need to be able to do.

    Machines aren’t any better. Go back to the bizarre ice cream flavors. Although the machine was able to figure out the pattern of ice cream names, nobody bothered to tell the AI that certain flavors just aren’t very yummy as ice cream. It was taught ingredients, but not ice cream specific ingredients.

    Trained task was much simpler than the real-world application

    In theory, it should be very easy to teach an AI how to drive a car. Program it with the rules of the road; teach it to identify lights and signals and road lines; and add some calculations for stopping distances and you’re good to go. However, we know that the reality of driving is much more complex and nuanced. In 2016, a self-driving car failed to recognize a flatbed truck as an obstacle and caused a fatal collision.

    Why?

    The car had been trained to drive on the highway, and as such, only recognized trucks from their front and rear view. The driver, however, kept the self-drive mode engaged on city streets. A semi-truck pulled out and crossed in front of the car. Thinking the truck was an overhead sign, the car did not stop.

    I can’t tell you how many times I’d get frustrated after looking at the results of my students’ assessments. Why were they not understanding the concepts I had taught for weeks? Honestly, the problem was not their lack of understanding. They understood exactly what I had taught them to understand. But what I had failed to do was put that understanding in a context of real-world use. We can teach math algorithms, or 5 paragraph essays, all day, but until they are shown how to adapt those concepts and apply them, they’re at a loss.

    So What?

    According to Shane, the best uses of AI are going to be with human supervision to make people more effective. AI will be used as a first draft tool but then humans will edit the results.

    AI is dumb, but teachers are not. We are adaptive. We may make some of the same initial mistakes as AI, but the difference is, we learn from them. We reflect, and we get better. The distance learning that happens this Fall will be hugely better than the distance learning provided in March.

    So take a deep breath, and remind yourself that you’re smarter than AI and you totally got this!