Tag: teaching

Affordances of IgniteAgent: My Super-Simple Observations on Using Agentic AI in Canvas

Affordances of IgniteAgent: My Super‑Simple Observations on Using Agentic AI in Canvas

When I first heard the phrase “agentic AI” I imagined a tiny digital butler, tuxedo‑clad, whisking through my virtual office, polishing assignments, refilling coffee cups (or at least the metaphorical ones), and whispering gentle reminders about overdue grades. Fast forward a few weeks, and I’m now living with IgniteAgent, the newest brainchild of the Canvas ecosystem, and I’ve got a front‑row seat to its uncanny ability to turn chaos into choreography. Below is my field report—supersimple, supersmart, and, yes, supersuasive—on how this little marvel is reshaping the life of an engineering communication instructor (that’s me) and, by extension, the whole learning‑management circus.

The “What‑Now‑Why‑How” of IgniteAgent

Before we dive into anecdotes, let’s get the basics out of the way. IgniteAgent is an agentic AI layer that sits atop Canvas, constantly monitoring, interpreting, and acting on data streams—course announcements, assignment submissions, discussion posts, calendar events, you name it. Unlike a static chatbot that waits for you to type a question, IgniteAgent proactively suggests actions, automates repetitive tasks, and even nudges students toward better learning habits. Think of it as a digital co‑pilot: you’re still steering the plane, but the co‑pilot handles the checklists, monitors turbulence, and occasionally cracks a joke over the intercom. The result? You spend less time wrestling with admin drudgery and more time doing what you love—teaching, mentoring, and maybe, just maybe, enjoying a lunch break that isn’t a sandwich‑in‑the‑office‑drawer affair.

Supersimple Automation: The “Set‑It‑and‑Forget‑It” Paradigm

My first love affair with IgniteAgent began with assignment grading rubrics. In an engineering communication class, I give students a mix of technical reports, oral presentations, and peer‑review critiques. Traditionally, I’d spend hours copying rubric criteria into Canvas, then manually adjusting scores after each submission. With IgniteAgent, I simply upload a master rubric once, tag the rubric with keywords (“technical clarity,” “visual storytelling”), and let IgniteAgent auto‑populate the rubric for every new assignment that matches those tags.The AI detects the assignment type,  and basic language metrics. I only need to fine‑tune the final numbers—a process that now takes minutes instead of days. The supersimple part? I never touch code, never learn a new scripting language. All configuration happens through an intuitive drag‑and‑drop UI that feels like arranging sticky notes on a whiteboard. If I ever get lost, IgniteAgent pops up a friendly tooltip: “Hey Shiva, looks like you’re trying to apply a rubric to a discusion post—did you mean a peer‑review matrix?” It’s like having a seasoned teaching assistant who knows my workflow better than I do.

Supersmart Insights: Turning Data Into Pedagogical Gold

Automation is great, but the real magic lies in insight generation. IgniteAgent continuously crunches data from three main sources: student interaction logs (clicks, time spent on resources); submission metadata (file types, revision counts); discussion sentiment analysis (tone, keyword density). From these streams, it surfaces actionable dashboards that answer questions I didn’t even know I had:

InsightHow It Helps Me
30% of the class never opened the effective visuals moduleI send a targeted reminder, embed a short video, and watch engagement jump to 70%
Students who submit drafts earlier tend to score 12% higher on final reports.I create a early-bird badge and see a 15% increase in early submissions.
Discussion sentiment dips after week 4.I schedule a live Q & A to address mounting confusion, smoothing the sentiment  curve.

These aren’t just pretty graphs; they’re decision‑making levers. By reacting to real‑time signals, I can adapt my syllabus on the fly, allocate office‑hour slots where they’re needed most, and even personalize feedback. Imagine telling a student, “Your draft shows strong technical depth, but your visual layout could use a splash of color—here’s a quick guide.” That level of granularity used to require manual review of each document; now IgniteAgent flags it for me automatically.

Supersuasive Communication: The AI as a Persuader

Engineering communication isn’t just about equations; it’s about persuasion—convincing stakeholders, drafting clear proposals, delivering compelling presentations. IgniteAgent helps me teach this subtle art in thre ways:

  1. Narrative Templates – The AI suggests story arcs (“Problem → Solution → Impact”) when students outline reports. It highlights missing elements (e.g., “Where’s your value proposition?”) and offers concise phrasing options.
  2. Rhetorical Scoring – By analyzing sentence structure, active voice usage, and rhetorical devices, IgniteAgent assigns a “Persuasion Score” alongside the technical grade. Students instantly see that a well‑structured argument can be as valuable as a flawless calculation.
  3. Peer‑Review Coaching – When students critique each other’s work, IgniteAgent provides a checklist of persuasive techniques to look for, turning peer review into a mini‑workshop on rhetoric.

The result? My class discussions have shifted from “Did you get the right answer?” to “How did you convince the reader?” The AI subtly nudges both me and my students toward a more holistic view of communication, where clarity and influence walk hand‑in‑hand.

The Human‑AI Partnership: Trust, Transparency, and Tinkering

No technology is a silver bullet, and I’m quick to admit that IgniteAgent sometimes over‑generalizes. Early on, it flagged a perfectly valid technical term as “jargon overload” because the word appeared frequently in a niche subfield. Rather than blindly accepting the suggestion, I tweaked the AI’s sensitivity settings, teaching it that in this context the term is essential, not excessive. Transparency is baked into the system: every recommendation comes with a confidence meter and a rationale snippet (“Based on 150 prior submissions, this phrase tends to lower readability scores”). This lets me decide whether to accept, reject, or modify the advice. Over time, the AI learns from my choices, becoming a personalized tutor for my own teaching style.

Trust also hinges on privacy. IgniteAgent processes data within the secure confines of Canvas, respecting the same end‑to‑end encryption that Proton is famous for. I never see raw student files; I only see aggregated insights. That peace of mind lets me focus on pedagogy rather than data‑governance headaches.

 From Chaos to Canvas: A Day in the Life (Post‑IgniteAgent)

Here’s a snapshot of a typical Monday now that IgniteAgent is my co‑pilot:

  • 8:00 am – Dashboard lights up with a gentle ping: “10% of students haven’t accessed the ‘Storyboarding’ resource.” I drop a quick 30‑second video teaser into the announcement bar; the access rate spikes within the hour.
  • 9:30 am – While reviewing draft reports, IgniteAgent highlights three submissions with low visual‑clarity scores. I add a comment, “Try using a consistent color palette—see the attached cheat sheet.”
  • 11:00 am – Live lecture begins. IgniteAgent monitors chat sentiment; halfway through, it alerts me, “Sentiment dip detected—students seem confused about the audience analysis section.” I pause, open a poll, and clarify the concept.
  • 2:00 pm – Office hours. Students receive personalized “next‑step” suggestions generated by IgniteAgent based on their latest drafts. One student smiles and says, “I finally know exactly what to improve!”
  • 4:00 pm – End of day. I glance at the weekly “Persuasion Score” trend line—up 8% from last week. I jot down a note to expand the rhetorical template library next month.

All of this feels effortless because the heavy lifting—data aggregation, pattern detection, reminder scheduling—is handled by the AI. I’m left with the human parts: empathy, nuance, and the occasional witty remark that keeps students engaged.

The Bigger Picture: Why Agentic AI Matters for Higher Ed

IgniteAgent is a microcosm of a broader shift: moving from static LMS platforms to dynamic, learning‑centric ecosystems. Traditional LMSs are repositories—places to dump syllabi, grades, and PDFs. Agentic AI transforms them into learning partners that anticipate needs, surface insights, and personalize pathways. For engineering communication courses, where the blend of technical rigor and expressive skill is delicate, this partnership is priceless. It ensures that technical precision isn’t sacrificed for storytelling, and vice versa; feedback loops are rapid, data‑driven, and scalable; and student agency is amplified—learners see concrete evidence of how their actions affect outcomes. In short, the AI doesn’t replace the instructor; it augments the instructor’s capacity to nurture both the engineer’s mind and the communicator’s heart.

Final Thoughts: Embrace the Agent, Keep the Soul

If you’re an instructor staring at a mountain of Canvas tabs, wondering how to keep up with grading, engagement, and curriculum tweaks, my advice is simple: let the agent do the grunt work, and you do the soul work. IgniteAgent (or any comparable agentic AI) excels at repetitive, data‑heavy tasks. Your expertise shines when you interpret insights, craft compelling narratives, and connect with students on a personal level. Remember, the AI is only as good as the prompts you give it and the trust you place in its recommendations. Treat it like a well‑trained apprentice—guide it, correct it, and celebrate its wins. Before long, you’ll find yourself with more time for research, creative lesson design, or—dare I say it—actually taking a coffee break without guilt. So here’s to a future where Canvas isn’t just a digital filing cabinet, but a living, breathing classroom assistant. May your rubrics be ever‑ready, your dashboards ever‑insightful, and your students forever inspired.

AI Snake Oil: Why I Stopped Trusting the Magic Show

I will tell you something that might sound silly at first: I used to believe in AI the same way people believe in unicorns, or diet pills, or that weird machine on late-night TV that promises rock-hard abs while you sit and eat chips. I believe that AI would save me time, make me smarter, polish my writing, analyze my research, help my students, and probably teach my classes while I napped. I really believed that. But here is the truth: it did not. It does not. And I am here to say it loud—I have stoped drinking the AI Kool-Aid, and guys, was it spiked with some sweet, slippery snake oil. See, AI came dressed up in a glittery jacket, threw around words like ‘efficiency,’ ‘automation,’ and ‘pedagogical revolution’ and made a lot of us clap like excited seals. I clapped too. Who would not want a shiny machine that could write lesson plans, grade essays, generate research questions, summarize books, cite sources, and whisper sweet academic nothings in your ear while you eat leftover spaghetti in front of a blinking cursor? But after the sparkle faded, I realized something: AI is not adding anything substantial to the real, deep, hard, delicious, frustrating, and soulful work of teaching or researching rhetoric. It’s like putting glitter on cardboard and calling it a Faberge egg.

Fig I: AI generated Image of a Unicorn that symbolizes epistemic purity and AI Snake Oil

Let me explain. I asked AI to help me brainstorm research questions. It gave me 10 questions that sounded like they were copied from a textbook written by a robot who would never read a real book. “How does digital rhetoric influence online learning environments? Wow! Groundbreaking! My cat could think of that. And she cannot even use a mouse without getting distracted by the screen saver. I needed curiosity, I needed fire. I got tepid bathwater. Then I asked AI to help me with student feedback. I thought maybe it could draft a few encouraging lines could personalize. What I got sounded like something from a sad greeting card factory where the writers had been replaced with soulless toasters. “Good job. Keep up the hard work.” Thanks. That’s the kind of thing that makes a student feel like a barcode. I tried to give AI a second chance. Maybe it was just having a bad data day. So I fed it more context. I told it the student was working on tone in professional emails. The response? “Try to be professional and use appropriate tone.” That’s like telling a chef, “Try not to burn it.” Thanks for the revolutionary insight. But I did not stop there. I went full nerd. I gave AI a complex rhetorical thoery prompt and asked it to draft a paragraph. What came back looked like a bored undergrad had Googled “rhetorical analysis” and copy-pasted the first paragraph of Wikipedia. I mean, sure, it had all the right words—logos, ethos, kairos—but it was all foam and no coffee. All bark, no bite. All sprinkle, no donut.

I began to wonder: what exactly is AI adding to the value chain of my research? Of my pedagogy? Of my rhetorical practice? The answer I arrived at—with a dramatic sigh and a slightly wilted sandwich in my hand—was: not much. Not yet. Maybe not ever. Because what I needed as a teacher, a writer, a thinker, a human—is not a sterile stream of regurgitated content. I need nuance. I need context. I need slowness. I need error. I need a student staring off into space, wrestling with an idea, and then lighting up like a firefly when it finally clicks. I need the mess. I love the mess. AI does not do mess. AI does averages. It smoothes everything out until nothing sticks. Nothing cuts. Nothing bleeds.

Let me say something that might get me kicked out of the 21st century: AI is not a collaborator. It is not a co-author. It is not a co-teacher. It is not a magical oracle of Delphi with a USB port. It is a calculator with a thesaurus. And sometimes it is a hallucinating calculator who makes up stuff and says  it with confidence, like that one kid in class who did not do the reading but still raises their hand. “But it is just a tool!” people say. Sure. So is a hammer. But if you use hammer to wash your dishes, your cups are going to cry. And that is the thing: AI is being used in the wrong rooms, for the wrong reasons, with the wrong expectations. We are asking it to inspire, to create, to feel, to reflect. But that is not what it does. What it does is imitate. And imitation, as far as I know, has never written a good poem, designed a good syllabus, or made a student feel truly seen.

Fig II: AI generated image of the Oracle

Let me give you a juicy example. I once asked AI to generate a short dialogue between Socrates ad Beyonce. Do not ask why. Just go with me. The result was a biege, baffling, boring exchange where Socrates said things like, “What is truth?” and Beyonce said, “Let’s empower women.” It was like watching a mime reenact philosophy night at Karaoke. No rhythm, no soul, no sass. Another time, I asked AI to help me generate metaphors for rhetoric. It gave me, I kid you not: “Rhetoric is like a bridge. It connects people.” Really? That is the best it could do? A bridge? I wanted fireworks. I wanted “Rhetoric is a mischievous racoon in a library of sacred scrolls.” Or “Rhetoric is a con artist with a PhD and a velvet tongue.” Something with some flair—some flavor—some garlic.  Instead, I got what AI always gives me: the blandest possible answer that no one will remember five minutes later.

So now, when someone says AI is transforming education, I tilt my head like a confused dog. Transforming it into what? A box of stale crackers? I am not saying AI can not do cool tricks. It can summarise articles. It can generate citations (sometimes fake ones, but hey, we have all had bad days). It can give you a to-do list. But so can a Post-it note. And Post-its do not pretend they are going to replace me. Because the magic of teaching—real teaching—is not just about information delivery. It is about relationship. It is about intuition. It is about awkward silences and big questions and the electric jolt when someome’s idea leaps off the page like it grew wings. AI can not do that. And let’s be honest, most of the time, it is not even trying.

The other day, a student told me, “I asked ChatGPT for help and it gave me a pretty good answer, but I still did not get it.” That is the whole point. Good teaching is not about answers. It’s about ways of thinking. It’s about questions that unravel you and slowly put you back together. AI does not know how to not know. It does not wrestle. It does not wonder. It just spits.

So I have decided: I am staying messy. I am staying human. I am keeping my sarcasm, my pauses, my sweaty palms, my failed metaphors, my joyful rambling, and my stubborn refusal to believe that a machine that has never loved or lost can teach anyone what it means to write well or think hard or care deeply. AI is fine for what it is—a tool. A digital Swiss army knife that sometimes forgets it is holding a spoon. But it is not the future of teaching. It is not about the soul of rhetoric. And it is definitely not the secret sauce of research. The sauce is still us: the long walks, the quiet mornings, the random napkin notes. The student who makes a joke that surprises you. The sentence that hits you so hard you stop and read it twice. That is real. That is deep. That is not artificial. That is the good stuff.

Therefore, let the AI talk. Let it type. Let it generate. I will be over here, with my pen, my paper, my voice, my students, my questions, and my beautiful, wild, irreducible human brain—doing the real work.

No snake oil is necessary.