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Essay assignments at home/Educator's Guide

Educator's Guide

Getting started with unsupervised activities

MATCHA's recipe for deterring AI misuse at home is to make the work process and follow-up reflection count as part of the grade. Complete Work History gives you detailed summary information about the entire process, including reading and editing. Assign points for providing adequate evidence of process. This at once rewards good work, deters AI misuse, and deters traditional plagiarism. For added security and an opportunity to deepen engagement, pair the essay with a MATCHA-managed in-person follow-up quiz.

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Step-by-step setup

  1. Set expectations and plan your grading rubric accordingly Make sure to communicate expectations to your students well in advance (consult your institution's policies for deadlines and other constraints). MATCHA is not going to deter AI misuse at home all by itself; it works together with appropriate course and grading policies. Some key points that should be part of your policies:
    • All work should be done in MATCHA, including reading and note-taking. Submitting a project with adequate evidence of work at all stages should be required for full marks. The at-home essay syllabus preset suggests giving 30% of marks for the work process.
    • Students should be required to add all documents they read to their library (using the Add to Library button) and to cite those specific library entries using the built-in citation tool (). This will allow you to more clearly correlate reading and citing. In addition, they should be asked to attach relevant passages as part of the citation properties. This is easy for an honest student to do, but hard for someone transcribing an essay. Ideally, full marks should require proper use of these citation tools.
    • An in-class follow-up quiz (facilitated by MATCHA) should be used in courses in which AI misuse is more likely. Full marks should require passing the quiz. See below for setup instructions.
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  2. Create your MATCHA course Create an account in the MATCHA Dashboard and create a course.
  3. Plan your readings and configure browser access

    The readings for your course should all be accessible through MATCHA. The MATCHA browser can access any online resources, but the best way to make readings available is to create a Reading Pack in MATCHA (in Activity Settings). This has four main advantages over LMS-hosted or publisher-hosted documents: password-free access from MATCHA, no access outside MATCHA (so you can track reading), more accurate reading tracking, and automatic addition to the MATCHA library for citing.

    If students need to read a book that you cannot add as a reading pack item, they can access it through an ebook provider. We currently recommend VitalSource for best results, with Google Play and Amazon Kindle as serviceable but non-ideal backups. In general, MATCHA has less-than-ideal tracking of reading on sites with embedded book readers (as opposed to normal PDFs), but it works with the readers on these three major sites. Make sure any books that need to be purchased are available through these platforms, and tell your students to purchase them there.

    You should prepare a browser allowlist if your students need to access resources that are not in reading packs or on the default allowlist (which includes the ebook sites just mentioned). Resources on your LMS or available through your library are common examples. To create a reusable allowlist, we recommend using the "Create allowlist" option from the Instructor Menu in MATCHA Writer.

    Once you have done this, view and adjust your browser configuration in Activity Settings / Browser. Make sure to select some Featured Links for students to use as starting points. Browser configurations can be reused between courses and activities, so this is mostly a one-time setup.
  4. Create and configure the activity

    Create an activity and enable the built-in browser with Complete Work History. Select the browser configuration you created in step 3. See below for suggested activity settings.

    Because your students may want to read throughout the term, not just when they are working on an essay, we suggest creating one MATCHA activity for each content module. A course is often structured in content modules. Written projects, whether essays or projects of other types, tend to cap modules composed of multiple units. If your course is organized in this way, it works best to name each MATCHA activity after a module and ask your students to do all the reading and writing for the module (notes, essay) in that activity.

    If your course has weekly or nearly weekly written activities (for example, responses to readings), it works best to create one activity for each week/unit. The activity is used for the week's reading and the eventual submission. You can also create separate activities for assignments such as essays.

    Ultimately, MATCHA is versatile. What matters is that all work (reading, note-taking) has a home in a MATCHA activity. If you enable Reading History Aggregation, a sub-feature of Complete Work History, MATCHA will aggregate the reading history from all submitted projects (as submitted) at the time of grading. When this is used, it does not matter if the reading is spread out across weekly activities, as long as it was submitted.

  5. Decide how students may use AI Decide whether students may use MATCHA's built-in AI assistant, and if so at what level. For unsupervised activities, an instructor-configured assistant can give students a permitted source of help inside MATCHA instead of pushing them toward outside AI tools.
  6. Add a follow-up quiz Ideally, pair the activity with a Follow-up quiz. If you choose to do so, you will need to design a reusable quiz in Activity Settings. We provide suggested questions to make this easy. Plan to review and edit AI-generated questions as part of the grading workflow (if grading using MATCHA, each student's quiz will appear as an additional tab in their project file). Decide in advance how the quiz will contribute to the rubric, focusing on students’ understanding, decisions, and reflection.

How to evaluate submissions and prevent AI misuse

There are two main approaches to submission evaluation with MATCHA, one more effective than the other.

The first approach is to use the MATCHA work record as an enhanced AI detector. The work metrics and complete replay provided by MATCHA will give you a much better sense of whether AI was used than any AI detection report based solely on the final product (and MATCHA does perform transcription detection on the edit stream). This is a possible approach, but it has the usual pitfalls of AI detection: the process of proving misconduct is fraught and arduous even with very strong evidence in hand.

The second approach, which we recommend, is to use the MATCHA work record to reward students' efforts in reading, taking notes, and revising their essays. After seeing a number of MATCHA submissions, you will have a very good sense of what the expected levels are on your assignments for character churn, reading time, time fragmentation, and other metrics provided by MATCHA. Award points for submitting a minimally plausible record of work. This penalizes and ultimately deters AI usage without your having to conclusively prove that anyone used it.

Ideally, use MATCHA Cloud submissions so that you can benefit from the Grading Tool in the Instructor menu, which can automatically flag submissions with low work metrics. If you are not using MATCHA Cloud submissions, open the projects you receive using the Open project for grading tool in the Instructor menu. When opened in this mode, a project displays the history analysis as the first tab you see. You can also open the same analysis using History > Analysis in any project. MATCHA's DOCX exports contain roughly the same information in an added preamble.

The most important metrics are the following:

Key work metrics
Metric What it shows Useful reference point
Reading time per word written Seconds the student spent reading in the browser for each word they wrote. For a typical essay, about 7 seconds per word (two hours per 1,000 words). Adapt this to the assignment.
Time fragmentation A measure of nonlinear writing and revision. MATCHA counts adjacent final-text positions whose characters were entered more than one minute apart, caps the raw proportion at 10%, and normalizes it to a 0–100% score. The time/location graph is also a useful visual check of time fragmentation but does not include Notes. At least 10–30%, depending on the activity.
Character churn The share of typed characters that did not make it into the final copy. Well-edited essays normally have at least 30–40% churn.

We suggest focusing on these metrics and rewarding students for meeting the expected thresholds. The specific weight given to each metric and the thresholds used should be adapted to your activity and students, but it is possible to devise a simple work-grading rubric that reliably rewards good work and penalizes low effort or cheating.

For a detailed explanation of the analysis and its metrics, see MATCHA's Project History Analysis Explained.

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