Greg Dillon – 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.

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.

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 Assessments LMS: Next-Gen Release

We’ve tightened core authentication: teachers and students can now connect Google Sign-In, making it easier to jump into any of the 12 apps (Étude, Test, Grammar, Writing, Word Study, Ordered List, Conversation, Chat, Forum, Media, Ventura, and the course hub) without managing multiple passwords.
Teachers get sharper control and visibility: My Students now links directly into a new Manage Enrolments matrix for one-click course assignments. Course pages display Canvas-style visibility badges so you can hide or reveal tasks instantly, and the student course view hides anything marked invisible. Submission dashboards in Étude, Test, Grammar, Conversation, Ordered List, and Writing now sort alphabetically and drop the developer-only raw JSON viewers, so grading workflows stay focused.
Live and asynchronous speaking tools are more authentic. Conversation tasks and upload workflows now coach students on cadence—natural rhythm, pauses, and intonation—plus we added an easy-to-copy student join link in Chat Monitor so hosts can share the live room with a single click. Chat’s monitor dashboard also highlights AI usage limits, host controls, and live transcripts for each room.
The LMS navigation feels smarter: “Login Preferences” is available to both teachers and students (with role-aware sidebars, help text, and instant Google linking), the teacher sidebar is scrollable and auto-hides developer tools unless you’re David Jones, and “Manage Rubrics” now supports copy-modify/delete operations with a single tap.
Media and forum workflows keep pace. Teachers can bulk-copy forums, toggle task visibility, and delete an entire forum plus its threads/posts with FK-safe cascading. The Create Task page has unified button styling, refreshed descriptions, and an updated “Coming Soon” panel (Survey, Audio Playlist, File on Server, Link to Webpage), making it easier to explain each element to staff.

App-by-App Highlights

Étude/Test/Grammar/Writing: Alphabetized submission rosters, lightweight UIs, AI license tracking in Grammar/Writing, and Google linking across all auth flows. Live tests record into test_task_student_responses, and we expose the schema dynamically via the updated inspect_schema.php.
Conversation (Record & Upload): Cadence coaching cards, Chromebook recording guides in the sidebar, admission logging, and Safari upload workflows that mirror the in-browser recorder.
Chat: Google Sign-In ready, live monitor with copyable join link, AI partner/peer modes, host controls (start/close, pair, reshuffle), and cadence-free text boxes (spellcheck disabled in chat and writ) to keep speech and writing authentic.
Forum: Manage Rubrics now clones/deletes as needed; forum visibility obeys course toggles; forum submissions keep their evaluation links; deleting a forum removes all threads/posts/AI usage.
Course Hub: Manage Enrolments grid, Canvas-style visibility icons, module-level controls, and student course views that hide anything not marked visible.
Media/Ventura/Word Study/Ordered List: Unified CTA styling, better descriptions, topic-specific guidance, and easy access to each element’s evaluation links from the student side.

Why It Matters

This release cements Innovation Assessments as a coherent suite rather than 12 separate tools. Authentication, visibility, enrolment, cadence training, grading, and schema inspection all work the same way across apps. Teachers can share tasks, manage rubrics, and run live sessions with less friction; students get natural speaking guidance, Google sign-in, and cleaner course views. We’re pushing the beta to customers now—watch for teacher-to-teacher sharing options (one-time copy codes) coming soon.

AI at Innovation: Three Ways Our Tools Support Teachers

Artificial intelligence isn’t here to replace teachers—it’s here to make their work more efficient, insightful, and impactful. At Innovation Assessments, we’ve built AI into our platform in three carefully designed ways. Each of these functions addresses a different part of the teaching cycle: preparing lessons, evaluating student work, and monitoring learning behaviors in real time.

Let’s take a closer look.

1. Teaching Assistant: Generating Prompts, Tasks, and Test Questions

Teachers often spend countless hours preparing materials: prompts for writing, comprehension tasks, practice questions, or even entire quizzes. Our AI-powered Teaching Assistant helps cut that prep time by generating high-quality starting points:

Assessment & activity prompts: Suggests open-ended discussion questions, role-play scenarios, or practice drills tailored to your subject.
Test question generation: Builds multiple-choice or short-answer items aligned to your chosen level and category, whether it’s social studies DBQs, French language tasks, or science practice sets.
Adaptability: Because the generator accepts teacher input on topic, difficulty, and format, you still set the pedagogical direction—the AI just does the heavy lifting.

The result? More time to focus on pedagogy and less on busywork.

2. Grading Assistant: Scoring Short Answers and Longer Essays

Grading is where AI can provide meaningful support without ever removing teacher authority. Our Grading Assistant uses OpenAI’s models to analyze student responses and offer suggested scores or rubric-based comments:

Short answer scoring: Provides a confidence-scaled score (e.g., full credit, partial credit) with a rationale tied to your rubric.
Essay analysis: Surfaces structure, clarity, and argument strengths/weaknesses so you can give students faster, more targeted feedback.
Teacher control: Every score is a suggestion—teachers make the final call. AI never replaces professional judgment.

This approach reduces turnaround time and makes it easier to give richer feedback, even on assignments with dozens of responses.

3. Proctor Function: Analyzing Student Activity in Online Apps

Digital classrooms introduce new challenges: how do you know if students are fully engaged, struggling, or even drifting off task? Our Proctor Function gives teachers insight into behavior patterns during online interactions:

Session monitoring: Tracks student activity logs (e.g., navigation events, copy/paste, time away from page).
Pattern analysis: Uses AI to highlight irregularities—like frequent page exits during a quiz—or flag potential academic integrity concerns.
Formative insights: Goes beyond “cheating detection” by helping you spot disengagement, pacing issues, or moments when students may need extra support.

Think of it as a lens into classroom dynamics that’s hard to see in a virtual environment.

Why These Three?

We chose these categories—Teaching Assistant, Grading Assistant, Proctor—because together they cover the full arc of digital instruction:

Before class (plan): generate engaging materials.
After class (assess): provide consistent, fast feedback.
During class (monitor): ensure students are active and supported.

Our guiding principle: AI should serve teachers, never the other way around.

Introducing Weighted Questions: The Smart Way to Grade Your Tests

Have you ever created a test where some questions were simply more important than others? Perhaps a single-sentence response question you intended as a quick check for understanding, and a more complex, multi-paragraph essay question that required a deeper analysis.

Or have you decided after the test that some questions need to be removed? Or correct the answer key and you need to recalculate scores?

With our latest update, you can now assign specific weights to each question on your tests, allowing you to create more nuanced and accurate assessments.

What’s New?

You now have the power to define the value of every question. When you’re editing your test, you can set the weight for each question, for example:

Multiple-choice questions worth 1 point.
Short answer questions worth 5 points.
An essay question worth 20 points.

Our new scoring system will automatically calculate the final score for each student based on the weights you set.

The Power of Re-Scoring

But what if you decide to change the weights after students have already taken the test? This is where the magic happens.

With the click of a single button, you can now re-score an entire class. Our new and improved algorithm will re-calculate every student’s grade, taking into account:

Updated Question Weights: If you change a question’s value, the scores will be instantly updated.
Answer Key Corrections: Did you find a mistake in the answer key? Correct it, and every student’s score will be recalculated.
Changes in Question Count: If you add or delete questions, the system will adjust the final score accordingly.

And it’s fast. This new re-scoring capability is built on a highly optimized system that can process hundreds of students and questions in a fraction of the time. We’ve even addressed edge cases, such as students who have had their old answers automatically archived, to ensure accurate and reliable results every time.

Our goal is to give you more flexibility and control over your assessments, so you can focus on teaching. Try out the new weighted questions feature today and see the difference.

NEW! AI Analysis of Proctor Notes (Student Engagement on Tests)

We’re excited to introduce a brand-new tool for teachers: AI Proctor Analysis. This feature takes the detailed proctoring logs collected during online assessments and automatically generates a professional, concise summary of student behavior—helping teachers spot issues faster and focus on teaching instead of sifting through logs.

How it Works

During an assessment, our system records digital behavior events such as page switches, text pasting, and other activity. These notes are stored securely in the teacher’s proctoring database.

With the new feature:

Logs are gathered – For each student and test, the platform collects all behavior notes.
Cleaned & organized – Duplicate or redundant entries are filtered so the report is readable.
Analyzed by AI – The logs are sent through our secure AI integration. The AI is instructed to act as a strict test proctor, highlighting suspicious or irregular activity.
Teacher summary – In just a few sentences, the AI generates a professional summary for the teacher, flagging potential problems and confirming if behavior was normal.

Why This Matters

Time-saving: No more scrolling through long behavior logs.
Professional tone: Reports are short, objective, and easy to share.
Enhanced oversight: Teachers get a clearer picture of digital test behavior at a glance.

Example

Instead of wading through dozens of raw log entries like:

Started task
Left page
Returned to test
Text pasted

The teacher sees a clear summary such as:

“The student briefly left the page twice and pasted text once. Behavior suggests potential use of outside resources. Recommend follow-up.”

Built with Security in Mind

Only authenticated teachers can access proctoring data.
Student activity logs are processed securely.
Every AI request is logged for accountability, including token usage and teacher identifiers.

The bottom line: With AI Proctor Analysis, you’ll spend less time interpreting logs and more time making informed decisions about your students’ online assessment behavior.