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
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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 (students are rewarded for showing their work). 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 by copying them from the clipboard when inserting a citation. 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, such as first- and second-year courses. Full marks should require passing the quiz. See below for setup instructions.
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Create your MATCHA course Create an account in the MATCHA Dashboard and create a course.
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Plan your readings and configure browser access
The mandatory readings for your course should all be accessible online through sites supported by MATCHA. MATCHA supports most websites, but some commercial ebook services work better than others for reading tracking. 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.
The best way to make available non-public readings is to create a Reading Pack in MATCHA (in Activity Settings). This has three main advantages over documents hosted on your LMS: password-free access from MATCHA, no access outside MATCHA (so you can track reading), and automatic addition to the MATCHA library for citing.
You should prepare a browser allowlist if your students need to access resources that are not in reading packs or on the default allowlist. 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. -
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 (this only works with MATCHA Cloud submissions and grading). When this is used, it does not matter if the reading is spread out across weekly activities, as long as it was submitted.
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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.
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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. Review AI-generated questions and preliminary evaluations before using them in grading.
Recommended activity settings for unproctored activities
- Proctoring: off. This is the normal choice for take-home or remote work.
- Availability, start and end: set dates and times. The availability times in MATCHA set hard limits for when students can work on an activity (or generate a submission document). The end time is not their deadline. It is the time past which you definitely do not need to accept more submissions. This is often set to a date after the course ends. We ask for an end time for housekeeping purposes, not to police deadlines. Deadlines are communicated using a separate, optional setting.
- Writing time: Set your activity's deadline here. This is for information only as deadlines are not currently enforced by MATCHA.
- Word limit: If desired, specify a word limit with possible enforcement. Note that MATCHA has no word limit in the Notes tab.
- Notes: enabled. Encourage students to use MATCHA notes rather than external tools.
- Browser: enabled. Normally, students need course readings, library pages, and your LMS. By using the built-in browser, they can create a record of their work process.
- Anonymous submission: optional. Use it only if anonymous grading is part of your process.
- External-Paste Block: enabled. This is one of the most important deterrents for unsupervised work because it prevents direct transfer from AI tools or other external sources. If you turn it off, externally pasted text is marked (it can be revealed using an option in the View menu and is reported in aggregate in various reports).
- Complete Work History: enabled. This captures detailed browsing history, enabling detailed reports of how much each cited (or uncited) work has been read. We consider this feature to be crucial for unsupervised work. If enabled, be sure to warn your students in advance and point them to our Student's Guide for information, because this feature has privacy implications. Make sure to include a reference to our Student's Guide in your syllabus.
- Reading History Aggregation: enabled. This results in the reading history of all assignments submitted through MATCHA Cloud being aggregated at the time of grading (with activity-specific reading still available). This is useful when your students are submitting several small assignments, such as weekly responses. This requires MATCHA Cloud submissions. Aggregated history is only visible in the built-in grading tool.
- AI Assistant: optional but recommended. To help discourage disallowed uses of AI, we recommend enabling MATCHA's AI assistant at a level that fits the assignment: grammar only, grammar and style, or full composition. If you want to help your students learn writing mechanics, turn on pedagogical mode. This makes the assistant act as a tutor rather than an auto-corrector. AI-made changes are tracked and reported in the Writing Analysis, so permitted AI use remains visible in the work record. Be explicit in your syllabus and activity instructions about what level of AI help is allowed.
- Follow-up quiz: enabled.
- Submission mode: MATCHA Cloud (.mcha). To facilitate the evaluation of submissions, use MATCHA Cloud submissions paired with our in-app grading tool, which facilitates the analysis and review of students' work. If you have to, you can use LMS-based submissions, but this will add steps for both you and students. We offer privacy options that keep submissions fully secure on MATCHA Cloud (see our Data Privacy page).
How to evaluate submissions
We advise instructors to give substantial weight to students' work records, including reading and editing. Do not think of MATCHA as an AI-detection tool, because drawing a bright line between suspect and non-suspect submissions is impossible. Some students whose minimal histories suggest that they might have used AI may, in fact, simply be doing the minimum amount of work. It will be difficult to distinguish between these two kinds of low-effort submissions. Instead, think of MATCHA as providing a complete record of work that you can grade. Students who do minimal work will be penalized alongside students who misuse AI, but that is acceptable because neither deserves a high grade.
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:
| Metric | What it shows | Useful reference point |
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| 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.