Innovation Blog

A Walk-Through of AI Chat at Innovation

Classroom AI Conversations with Guardrails, Structure, and Teacher Confidence

One of the questions teachers ask most often about classroom AI is not “Can it chat?” but “Can I trust it enough to use it with students?” That is exactly the problem our AI Chat app was built to solve.

The goal of AI Chat is not to hand students an open-ended chatbot and hope for the best. The goal is to give teachers a way to use AI conversation as an instructional tool inside a structured classroom environment, with clear prompts, strong boundaries, and teacher-facing oversight.

The teacher begins by designing the experience. Instead of sending students into a blank AI space, the teacher sets the context for the chat lesson. That can include the topic, the role the AI should play, the style of interaction, and the kind of responses students should practice. In other words, the teacher is not losing control of the lesson. The teacher is shaping it. The AI becomes part of the instructional design, not a replacement for it.

That design layer matters because it changes the tone of classroom AI use completely. A good AI classroom tool should not start with “Ask anything.” It should start with “Here is the conversation space, the purpose, the boundaries, and the learning goal.” AI Chat does that by grounding the experience in teacher-authored prompts and lesson framing.

Safety and guardrails are where confidence really begins. In a classroom setting, teachers need to know that the AI interaction is not just interesting but manageable. AI Chat is built with that in mind. The interaction is task-based, teacher-directed, and contained inside the app’s lesson structure. That means students are not wandering through a general consumer AI environment. They are participating in a bounded academic conversation designed for class use.

Students do not need “more AI.” They need a clear task, a safe place to respond, and a sense of what the conversation is supposed to accomplish.

Another confidence point is that AI Chat is not just about what students see. It is also about what teachers can supervise. Classroom AI becomes much more usable when teachers know there is visibility into the work. A safe AI lesson is not only about preventing bad outcomes; it is also about preserving teacher awareness. If a tool gives structure without visibility, teachers still hesitate. AI Chat is designed to keep the instructional frame intact so the AI supports the lesson rather than taking it over.

The prompt layer is especially important here. Teachers can shape the AI to behave more like a tutor, conversation partner, role-play partner, or guided practice engine depending on the activity. That means a teacher can create targeted uses for AI instead of generic ones. In one lesson, the AI might support language practice. In another, it might guide historical role-play. In another, it might help students think through an argument or reflect on a reading. The key point is that the teacher defines the academic purpose first.

That structure also helps address one of the biggest concerns around classroom AI: unpredictability. Teachers are much more likely to use AI confidently when they know the task is framed, the expectations are clear, and the AI’s role is intentionally constrained. AI Chat supports that by centering the prompt design and lesson purpose rather than offering unrestricted exploration as the default.

There is also a practical classroom benefit to this kind of design: it reduces the intimidation factor for both students and teachers. Students do not need “more AI.” They need a clear task, a safe place to respond, and a sense of what the conversation is supposed to accomplish. Many teachers feel the same way. AI Chat makes classroom use feel more like a guided lesson and less like opening the door to an unknown system.

This approach promotes confidence without pretending AI needs no supervision. It respects the reality that teachers want innovation, but they also want boundaries. They want students to interact with AI, but not in a way that feels chaotic, untraceable, or disconnected from the lesson. AI Chat works because it treats safety, prompt design, and teacher control as core features, not optional extras.

In short, AI Chat is built to help teachers bring AI into the classroom with more confidence. It combines instructional prompting, structured interaction, and classroom-minded guardrails so teachers can use AI as part of a lesson without feeling like they are surrendering the lesson to the tool.

Convo Application Walkthrough

One of the most practical tools in Innovation Assessments is Convo, our speaking task app built for teachers who want students to respond to prompts in a more authentic, manageable, and scoreable way.

At its core, Convo is simple: the teacher creates a conversation task using a sequence of prompt audios, students listen and respond one prompt at a time, and the teacher can later monitor progress, review submissions, and score the work using either simple prompt-by-prompt scoring or a fuller rubric workflow. But what makes the app useful is how much classroom reality it accounts for.

A teacher begins by configuring the task. The setup is intentionally straightforward: give the task a title, add context or directions, set an overall time limit, and decide whether each prompt response should also have its own time cap. That matters in speaking assessment, because sometimes you want students to think and answer naturally, not rehearse for several minutes. Teachers can also decide how students will complete the task. There is a browser-recording version for direct in-app speaking, and there is also an upload version for cases where device compatibility or student circumstances make recording in-browser less reliable. If a teacher wants tighter control, the task can require in-browser recording so response timing is enforced more strictly.

The prompt-building process is also flexible. Teachers can upload prompt audio files or record prompt audio directly in the browser while building the task. Each prompt can include a memo or script, which helps keep the assessment organized and teacher-friendly. This makes Convo work well for world languages, oral interpretation, speaking checks, listening-response tasks, and even teacher-created mock interview activities.

On the student side, the experience is designed to be focused. Students open the task, hear the teacher’s audio prompt, and respond prompt by prompt. The app supports real classroom constraints: access and visibility checks, timing, saved progress, and submission tracking are all built into the workflow. Students who have already submitted are not accidentally allowed to start over unless the teacher readmits them. That matters because speaking tasks can otherwise become messy very quickly if students are unsure whether they are still “in progress” or already finished.

Another strength of Convo is that it does not pretend every device behaves the same way. The app supports both browser recording and upload-based response collection, which gives teachers a practical fallback path when needed. In real schools, that matters more than elegant theory. A speaking tool only works if students can actually complete the task on the devices they have.

From the teacher side, monitoring is lightweight and useful. The teacher can open the monitor view and see which students are in progress, how many prompts they have completed, and who may need a readmit. This is helpful during live class use, language lab work, remote learning, or make-up assignments. The monitor is not overloaded; it gives the teacher enough visibility to manage the task without turning into a distraction.

Scoring is where Convo becomes especially flexible. Some teachers want quick scoring by prompt, especially when they are listening for completion, clarity, or general performance. Others want a more formal evaluation process. Convo supports both. A teacher can score by individual response or switch into rubric-based scoring, depending on how the assessment is designed. That means the same app can support quick formative checks and more structured summative speaking assessments.

There is also a strong accountability layer behind the scenes. Convo includes proctor-style event logging, submission tracking, and workflow protections that help preserve the integrity of the task. That is particularly useful for graded speaking work, asynchronous assessment, and remote completion settings where teachers want a clearer record of how the task was completed.

What I like most about Convo is that it is not built around a fantasy classroom. It is built around the real one. Teachers need prompt audio options. Students need a focused workflow. Some devices cooperate; some do not. Some speaking tasks need strict time limits; some need flexibility. Some teachers want quick scoring; some want rubric-driven feedback. Convo makes room for all of that.

In short, Convo is a speaking assessment tool designed for actual classroom use: easy to configure, realistic for students, adaptable across devices, and strong on both monitoring and scoring. It helps teachers move beyond “just record something and upload it” toward a cleaner, more intentional speaking workflow.

Introducing SlideCraft: Collaborative Presentations Without the Formatting Distraction

One of the most effective ways for students to master new content is to own it. When a student has to synthesize a topic, identify what matters, and teach it back to their peers, the learning sticks.

However, in a typical classroom, “making a presentation” often turns into a week-long odyssey of font choices, transitions, and image cropping. The actual thinking—the synthesis—gets buried under the formatting.

That’s why we built SlideCraft. It’s a new tool within Innovation Assessments designed for speed, accountability, and meaningful participation. It’s not a full-featured slide editor; it’s a structured workflow that turns a class’s collective research into a ready-to-present deck in minutes.

The Problem with “Death by PowerPoint” (and Canva, and Slides…)

In many EdTech tools, “engagement” is equated with gamification—points, music, and flashy animations. At Innovation, we believe real engagement is cognitive load. We want students focusing on the history, the science, or the literature, not the “rules of the game” or the aesthetic of a slide border.

SlideCraft is built for a specific, powerful classroom pattern:

The Hook: The teacher introduces a topic.
The Task: Students are assigned specific subtopics or “jigsaw” pieces.
The Build: Students research quickly and build exactly one slide.
The Share: The class presents the completed, unified deck immediately.

How It Works: Designed for the Live Classroom

SlideCraft lives in two places: your prep time and your live instruction.

Teacher Setup (The Prep) In configuration, you build the skeleton of the lesson. You can add up to five starter slides (intro, instructions, or framing) and then define the “prompts” students will receive. These prompts are reusable, meaning you can run the same activity with five different sections without rebuilding the wheel.

The Live Session (The Action) When class starts, you launch the Live Host from your course playlist. Students join via a link from their login page and are automatically assigned one of your prompts.

As they work, you can:

Monitor incoming drafts in real-time.
Set a countdown timer or stop the session manually.
Autosave everything: Because this is built for real-school Wi-Fi and interruptions, student work is preserved constantly as they type.

What Students See: Focus over Frills

The student interface is intentionally lean. There are no menus for “WordArt” or background gradients. Students see:

Their assigned title and specific instructions.
A field for concise bullet points.
An image upload (optional).
A Source URL field: This is critical. By making the source a required part of the “Craft,” we reinforce academic integrity from the first click.

From “Building” to “Presenting” in One Click

The moment you stop the build session, the host view transforms into a presentation stage.

The finished deck is automatically assembled: your intro slides first, followed by the student-generated content. During the presentation, the teacher has access to a Presenter Timer and a Show Sources toggle. This allows you to pause the lesson and discuss source credibility or authority on the fly—turning a student slide into a teachable moment about information literacy.

Accountability and Scoring

SlideCraft isn’t just an “activity”—it’s an assessment. Once the presentation is over, the work doesn’t disappear. All student submissions are saved for review. Using the familiar Submissions and Score tools, you can:

Evaluate slides using your existing rubrics.
Score based on the quality of the bullets and the reliability of the sources.
Provide written feedback and release evaluations to students.

A First Use Case: The French Revolution

Imagine a lesson on the causes of the French Revolution.

Teacher Intro: 3 slides on the monarchy and the Three Estates.
The Build: Students are assigned prompts like The Bread Crisis, Enlightenment Ideas, The American Influence, and Louis XVI’s Debt.
The Result: Within 15 minutes, you have a 25-slide deck built by the class.

You aren’t just lecturing; the students are providing the evidence.

SlideCraft fills the gap between passive slide-viewing and time-consuming independent projects. It’s built for teachers who want their students to be active, collaborative, and accountable—without the “formatting fatigue.”

If you’re ready to turn your next research burst into a live class product, SlideCraft is ready for you in the Innovation dashboard.

EduTech from a Teacher-Coder: Engagement Without the Game

How to create meaningful, real-time engagement with a workflow that’s simple and actually usable in class

Whether you teach remotely like I do or are working in-person, you know that student engagement in the lesson is a paramount concern. It is important that students not be passive recipients very often or for long periods.

Gamification enthusiasts and coders who have not taught middle school seem to often believe that the answer is to make studying something more like an XBox adventure. Add music, competition, points and tokens and they will learn without even knowing it!

But I want my students’ cognitive load carrying the lesson, not the rules of the game or the points they earned or the banter with the other team. To this end, I developed “live session” interactive versions of many of the Innovation apps.

The workflow goes like this: the instructor starts a host instance of the activity, copies a special participation link and send it to students, who then get a screen for interacting. Live sessions turn the activity into an interactive activity that fosters engagement through inquiry, curiosity, discussion, debate, reinforcement.

I use the TestApp and the Étude live sessions to debrief after a test or to review for tests. the teacher screen displays the questions one at a time. The teacher host opens the session to responses and closes after the time. Student responses are displayed anonymously for debriefing.

“Engagement isn’t just activity—it’s thinking.”

I use the Grammar app live session in my French classes. I can display the prompt to the screen, open for student responses, they then submit their work and I can display anonymously for debriefing. This is exactly the same as the assignment, just displayed in a different interactive form.

The Media powerpoint application I use most often for teaching social studies and for my advanced French courses where I am delivering content. This is a very powerful and flexible application that will be discussed in detail in a later post. Suffice it to say for now that the media live sessions have all the tools we need to get brief and extended student replies and reactions, from short answer to multiple-choice and even a selection of emojis!

One of my students remarked that the live sessions were kind of a boring Kahoot! I laughed and replied that was the intention! No points, music, sound effects, rankings, scores, goofy animations. The focus is on the lesson. If anything is to be entertaining, it’s going to be me!

EduTech from a Teacher-Coder: Restoring the Teacher’s Line of Sight

For about a decade, classroom technology quietly broke something important.

Teachers lost their line of sight into student work.

I don’t mean theory—I mean the simple ability to know what students are actually doing.

Some call this “command and control,” but that misses the point.

What teachers actually need is simple: the ability to know what students are doing, in real time, so they can guide and support them.

We need the old fashioned line-of-sight supervision and guidance that instructors maintained in effective classrooms in the ages before every student got a ChromeBook with a pile of office productivity software. I knew exactly what my students were doing as I circulated the classroom. I could look over their shoulder and contribute advice to a forming essay. I could redirect students who found something off-task more interesting to do. I could ensure with some reliability that no cheat sheets were being used on tests and that students were doing their own work. I was able to keep the class workflow moving so we didn’t fall behind with delays and procrastination.

Then came ChromeBooks whose screens we could not see or were easily hidden. With that came office productivity tools designed for mature adults in paying jobs who were motivated for the most part to get their work done. Ironically, tools designed for productive adults often made classrooms less productive.

There are a number of expensive software on the market now for monitoring student screens. At my last district, we had a product that let us monitor everyone’s tabs. But I really don’t think my own workflow is much improved by surveilling a dozen tiny screencasts.

I’m retired now and I teach remotely a few hours a day. I need more than ever to know know exactly what my students are doing. It is important to maintain the pace of the lesson and to ensure assessment integrity. This post’s “EduTech from a Teacher-Coder” is the monitors and proctors in all of Innovation’s apps.

Monitor

Every application at Innovation comes with a monitor to display in real time how students are progressing on their task. The test monitor shows what question students are on and even has a messaging feature so I can quietly post notifications to students in their test. The writing app monitor displays the current essay for each student, the number of words, their use of any AI licenses. Vocabulary quiz, sorting app, the “KnowWhere” map study, cause and effect study, reading comprehension, cloze app, ordered list, forum, even the AI chat application can display student progress and often their work product. The monitors all hide the student names as an option so that teachers can display the monitor on shared screen or in front of the classroom as a way to remind everyone to keep pace.

Monitors let teachers see the correct responses for many activities. The monitor returns an important feature of command and control of the classroom: I need to know exactly what they are doing.

Proctor

The proctoring feature is extensive throughout all of the activities. Proctor is an after-the-fact kind of analysis and proctors come with AI interpretation and summary features. When did they start the app? How long did they spend on each question? Did they leave the screen? Paste in any text? Try to right-click and use a spellchecker or AI assistant not licensed?

Common thoughts on giving assessments in remote teaching are that it is not reliable. But if there is a strong AI-assisted proctor running during the assessment and there is an adult supervising in the room, we can be assured of an assessment result as reliable as old fashioned in-person classes.

Teacher Command and Control Supports Successful Student Outcomes

When the proper guardrails are in place, guardrails we have always had in teaching, then we can be more assured of delivering the kind of high quality, effective training that leads to student success. A dozen applications at Innovation include monitoring and they all include proctoring.

For years, we handed students powerful tools…
and took away the teacher’s ability to see how they were being used.

That was the mistake and now we’re correcting it.

A Better Way to Assign Short Student Presentations Across the Curriculum

When I started teaching in 1991, the highest level of technology in my class was my pocket calculator. Supervision was a matter of circulating the room to make sure students were engaged.

When technology became part of our schoolrooms, we had to surrender a lot of the supervision that we used to have. Students could now hide behind ChromeBooks or click away quickly when we walk by and easily become off-task and disengaged. The main reason for this was that the first technology solutions were designed for offices, not for classrooms. We thought this was a great idea, since many students would one day in the workforce be using such applications.

We were wrong about that.

Software designed for adults, for office workers and designers, is not appropriate for most classroom settings simply because it does not have the guardrails and monitoring that we used to have in pre-EdTech days.

Yes, we worked around it. We added internet filters, screen monitoring software, and the like. But that is not the same as having direct observation of our students and control over their workflows.

Many efforts to create truly classroom-friendly EdTech have focused on “gamifying” learning. Developers believed in the old trope that you could trick them into learning if they were having fun. Don’t get me started on that…

The problem I wanted to address in this post occurred in a remote AP French class I was teaching. The remote platform was Canvas. The assignment was to produce a 2-minute video presentation in French, mostly improvised, to model how the task was set up on the AP exam. The students dutifully uploaded their little videos to Canvas and it was obvious that they were reading prepared scripts and they had either an AI either do the work or correct the work. I knew from class sessions that they were not capable of that level of language proficiency and anyone watching could see they were reading.

How does one rationalize giving a high stakes grade for that?

EduTech Solution from a Teacher-Coder

Presto is an application at the Innovation platform that resolves the issue of students having AI-generated presentations and scripts without real learning or synthesis. While originally devised as an evaluation tool for world language learners, it is extremely effective in content area classes like social studies.

Students log in and are redirected to the assessment. After setting their camera and mic and starting the camera, the task begins. Only now can they see the prompts. There is a strict timer and an AI-enhanced proctor records their engagement and activity on the page. There is a time limit. Once started, they need to finish or they must be readmitted by the teacher. This prevents viewing the prompts and then starting again after research.

The proctor provides the supervision we often lack in modern education software. The time limit and the coordination of camera activation with prompt visibility prevent cheating very effectively.

“AI has made scripted assignments meaningless. Presto measures thinking instead.”

More importantly, the structure encourages authentic thinking. Students must interpret the prompts and organize their ideas in real time rather than relying on pre-written scripts. Instead of reading polished AI-generated text, they must explain ideas in their own words within a clear time limit.

For teachers, this makes evaluation more meaningful: the focus shifts from detecting AI assistance to assessing a student’s ability to communicate understanding.

Students must interpret the prompts and organize their ideas in real time. Instead of reading polished AI-generated text, they explain ideas in their own words within a clear time limit.

For teachers, this changes the evaluation process completely. Instead of trying to determine whether a script was written by the student or by an AI assistant, we can focus on what actually matters: a student’s ability to communicate understanding.

And that was the goal all along.

The Growth Bonus: Rewarding Improvement While Maintaining Academic Standards

Two students submit essays that both receive a score of 75.

At first glance, their performance appears identical. But the stories behind those two scores may be very different. One student might have scored a 74 on the previous assignment—essentially maintaining the same level of work. Another might have improved dramatically from a 60.

In both cases the essays themselves may be similar in quality. Yet one student clearly demonstrated substantial learning along the way.

This raises an interesting question for teachers: should grades reflect only the current piece of work, or should they also recognize improvement over time?

In many courses, particularly those that emphasize writing and analytical thinking, improvement is an important part of the learning process. Students revise strategies, incorporate feedback, and gradually strengthen their arguments and use of evidence.

To recognize that progress without distorting the meaning of grades, some assignments may include what we call a growth bonus.

The idea is simple: meaningful improvement deserves recognition—but the quality of the current work must still matter most.

How the Growth Bonus Works

The growth bonus uses a mathematical rule that compares the current score with a previous comparable assignment.

Three values are involved:

R – the raw score on the current assignment
B – the score from a previous assignment
T – a readiness target representing strong course-level work (often around 82)

The adjusted score is calculated as:

Adjusted = max(R, R + 0.8 × max(0, R − B) − 0.2 × max(0, T − R))

In plain language, the formula does three things at the same time.

First, it rewards improvement from the previous assignment. If a student improves by ten points, most of that improvement is reflected in the adjustment.

Second, it moderates extremely large score jumps when the current essay is still below the level expected for the course. This keeps the adjustment from turning a developing essay into a top-tier score.

Finally—and importantly—the formula guarantees that the adjusted score can never be lower than the original score.

The growth bonus can help a score. It cannot hurt it.

A Quick Example

Suppose a student scored 61 on a previous essay and 72 on the current one.

The improvement is:

72 − 61 = 11

Most of that improvement is rewarded:

0.8 × 11 = 8.8

Because the essay is still somewhat below the readiness target of 82, a small moderating adjustment is applied:

0.2 × (82 − 72) = 2

The adjusted score becomes:

72 + 8.8 − 2 = 78.8

The student’s improvement is recognized, but the final score still reflects the level of the current work.

What Happens If the Score Declines?

If the new score is lower than the previous one, the improvement term becomes zero. In theory the formula could produce a slightly lower number—but the rule

max(R, …)

ensures that the final score never drops below the original score.

In practice, this simply means the raw score stands as it is.

Why Not Just Use Standardization?

This approach adjusts scores based on the statistical distribution of scores in the class.

A simplified version of the formula looks like this:

Standardized score = ((R − μ) / σ) × s + m

Here:

R is the raw score,
μ is the class average,
σ is the standard deviation,
and the constants s and m determine the new spread and average of the scores.

Standardization can be useful when a test turns out to be unusually difficult or unusually easy. However, it measures performance relative to the class rather than improvement over time.

In some cases it can also produce surprisingly large adjustments. A raw score in the low seventies might become a ninety simply because the class average was low.

The growth bonus approach focuses instead on learning progress—recognizing students who improve while still keeping grades tied closely to the quality of the work itself.

Why the Readiness Target Matters

The readiness target used in the formula—often around 82—represents the level of performance typically associated with strong work on AP-style writing rubrics.

It is not a passing threshold or a minimum expectation. Instead, it serves as a reference point that helps keep score adjustments realistic.

Students who are already writing at a strong level will see modest adjustments. Students who are improving rapidly will see more noticeable ones.

The Larger Goal

Ultimately, the purpose of the growth bonus is not to inflate grades. It is to encourage the kinds of behaviors that lead to real academic progress: revising writing strategies, strengthening arguments, integrating evidence more effectively, and improving clarity and precision of language.

Grades should communicate meaningful information about learning. They should reflect both where a student stands today and how far that student has come.

The growth bonus is one way of recognizing both.

The Classroom Is Not a Game (and Not an Office Either)

Though retired, I still teach a few courses a day remotely. This week, I attended a professional development meeting for one of the companies for whom I teach, where a presenter used a popular interactive presenting app. The presentation itself was excellent. The app, however, was another matter entirely.

I will grant that, as a developer of educational technology myself, I am a harsh critic. But I suspect even the hundred and fifty or so others on that Zoom call would agree. The app was heavily gamified, filled with sound effects and floating reaction emojis designed to promote “engagement.” Each emoji triggered a popping bubble sound as it drifted across the screen. Participants continued clicking them even after being asked to stop, while the presenter was attempting to explain how to construct a complex AI prompt. The result was not engagement, but distraction.

My earlier posts have noted my long-standing skepticism of gamification. Its promoters often cling to the old trope that if students are having fun, they will not even realize they are learning. Forgive me for sounding like the old fogey that I am, but that idea has always struck me as pedagogically misguided. I want students to know they are learning. More importantly, I want them to learn how to guide and regulate their own learning. Attention should be directed toward the material, not toward points, sounds, or game mechanics.

If you explore the Innovation platform, you will notice that it is intentionally plain. Interactive tools include emoji responses, but they are subtle, silent, and easily disabled. This is by design. The platform reflects how I actually teach, rather than how a game designer imagines learning should feel.

Because most teachers are not developers, we often adapt software that was never designed for classrooms in the first place. We rely on office productivity tools or on educational software built by developer teams whose instincts lean more toward gaming than pedagogy. I occupy an unusual position as both teacher and developer, and I find great satisfaction in coding applications that behave the way a teacher actually needs them to behave.

The Classroom is Not the Office

Having taught since 1991, I have lived through the entire technological transformation of education. My first classroom had chalkboards and binders. My last, before retiring three years ago, had 1:1 student laptops and a SmartBoard. One persistent problem has been that much of our classroom software originated outside education, particularly in office environments.

When we placed laptops running word processors and spreadsheets in front of students, we gained powerful tools but lost a degree of visibility and supervision. In 1991, it was nearly impossible for a student to hide off-task behavior behind a notebook. In 2026, it may be a hidden browser tab. What was marketed as “real-world experience” often came at the cost of instructional control.

At Innovation, I aim to design learning spaces that originate in education rather than being imported from the office or the gaming world. Our writing tools include optional AI proctoring and live monitoring so instructors can observe student work in progress. Our assessment tools provide similar oversight, along with messaging features that allow teachers to guide, redirect, or support students in real time.

In short, the goal is not to make learning noisier or more entertaining. It is to make it more focused, more observable, and more teachable.

Good educational technology should not compete with the lesson for attention. It should support the teacher, clarify the task, and fade quietly into the background of learning.

After more than three decades in the classroom, I have come to believe that the best tools are not the loudest or the most entertaining, but the ones that respect how learning actually happens: through focus, guidance, and sustained attention. If our software cannot preserve those conditions, then no amount of animation, gamification, or sound effects will make up for what is lost.

Precision in Assessment: Why Standardization Outperforms the Traditional “Curve”

In secondary and post-secondary education, teachers often face a “measurement gap.” This occurs when a highly rigorous assessment—such as a mock professional exam or a complex technical project—yields raw scores that accurately reflect performance benchmarks but fail to align with the broader institutional grading scale.

To bridge this gap, many educators rely on a “curve.” However, traditional curving often lacks statistical validity. Standardization, specifically through the use of Z-scores, offers a more mathematically sound and equitable alternative.

The Limitations of Common “Curves”

The term “curve” is frequently applied to two common but flawed methods:

The Flat-Point Addition: Adding a set number of percentage points to every student. While “fair” in its uniformity, it does nothing to address the variance or “spread” of the scores.
The Ceiling Curve: Adjusting the highest score to 100% and shifting others accordingly. This makes the entire class’s grades dependent on a single outlier, which can lead to volatile and inconsistent results.

These methods are essentially “band-aids” that fail to account for the relative performance of the cohort.

The Logic of Standardization (Z-Scores)

Standardization treats a set of scores as a distribution. By converting raw scores into Z-scores, we determine exactly how many standard deviations a student’s performance sits above or below the group mean.

The formula for calculating a Z-score is: z = (x – μ) / σ (Where x is the raw score, μ is the mean, and σ is the standard deviation.)

Once we have the Z-score, we can “re-map” it onto a target distribution (such as a school’s historical GPA mean). This ensures that a student who performs at the 90th percentile on a difficult assessment is rewarded with a grade that reflects that 90th-percentile standing in the gradebook.

Why Standardization is the Professional Choice

Maintains Rubric Integrity: Educators can grade with extreme rigor against high-level standards without fear of destroying a student’s GPA. The raw feedback remains honest, while the gradebook remains fair.
Corrects for Assessment Difficulty: Not every test is of equal difficulty. Standardization automatically adjusts for a test that was “too hard” or “too easy” by focusing on the student’s relative mastery within the cohort.
Statistical Defensibility: If a grade is challenged, the educator can point to a transparent, mathematical process based on the class distribution rather than an arbitrary “bump” in points.

By adopting standardization, we move away from “adjusting numbers” and toward “aligning distributions.” This practice respects the data produced by the assessment while ensuring that the final grade accurately reflects a student’s standing within the academic environment.

Innovation 2.0

The few who read this may have seen the post a while back called “Sunset“in which I reflected on the difficulties and, well, failures I suppose of trying to develop an LMS as a small business without a huge bankroll for a coding team and marketing. In 2007 when I started this and made some money from my inventions, the internet was very different.

So then AI came along. There is plenty of material for blog posts on how this transforms my teaching (I still teach remotely part-time). The big effort for me was trying to devise ways to prevent or at least make difficult the inappropriate use of AI by my students. Interestingly, I turned to AI to do this.

Like my colleagues who did not just surrender to AI student work submissions, I first worked on changing how I designed my assignments. That only goes so far.

Next I rolled up my sleeves and started tweaking my own code in this platform which I use for teaching remotely. Things like timers, detailed logging and response of student activity in a browser, hiding things until time has passed, and eventually on to getting an API key from OpenAI so that I could add a button that would analyze the logged data from student interactions on the platform and understand likelihood of inappropriate usage.

Once I started tweaking my old code, I noticed increasingly that the AI I was using to correct it, making enhancement above my coding ability, was itself increasingly having trouble with old-fashioned and out of date coding practices in the Perl language. I asked it about this. It explained that the code base I had (which is admittedly 20+ years old) was out of date such that it would not support a moderate customer base. The database itself, holding the work of myself and customers some going back twenty years, had obsolete features beyond the scope of this post to explain. The work to re-code and update this was enormous and overwhelming. That’s when the “Sunset” blog was written.

But then I had a cool idea for an application. I needed a way to let my AP French students practice and be evaluated asynchronously for conversation skills. I wanted to write this in a modern way using up-to-date code base. I used AI to write it. I was not as proficient in PHP as I was in Perl. I was tired of coding and wanted to focus on curriculum development.

The result was smashing! And from there I kept building… Three months later, I have nearly completed Innovation 2.0. Wow. I have moved from coding myself to directing the AI to to the detailed coding. I am now the creative director, no longer consulting programming language manuals or searching stackoverflow.

What’s especially exciting for me is that the new software works exactly as I wish it to. And it’s all in one place! That was why I started coding 30 years ago anyway! I like to build and invent.

So in January I will be using innovation 2.0 with my own students to refine and debug it and then move customers over in February and start offering this platform publicly. There are great new apps I can offer, a fully-integrated AI support system with guardrails and controls, effective live monitoring and more!