Inclusion classrooms staffed by a general education teacher and a special education co-teacher present a genuinely distinct grading challenge, since both teachers may be reviewing and adjusting student writing feedback, but without a shared, coordinated system, they can easily duplicate effort or apply inconsistent adjustments to the same student's work. A general education teacher configuring an AI grading tool independently, without input from the co-teacher, risks building a rubric that does not adequately account for the range of accommodations and modifications present across an inclusion classroom's student population. Coordinating this configuration explicitly between both teachers protects against a fragmented, inconsistent grading experience for students.

The most effective co-teaching pairs treat AI-assisted grading configuration as a shared responsibility from the outset, with the special education co-teacher bringing specific knowledge of individual student accommodations and modifications that the general education teacher may not track as closely day to day. This division of labor mirrors how effective co-teaching already works in most inclusion classrooms, where each teacher contributes distinct expertise to a shared instructional responsibility. Extending that same collaborative model to AI-assisted grading configuration keeps the practice consistent with how the classroom already operates.

Clear communication between co-teachers about who reviews and adjusts AI-generated feedback for which students prevents the kind of duplicated or contradictory adjustments that can otherwise confuse students receiving feedback from two different adults. A simple, explicit agreement, the special education co-teacher reviews feedback for students with an IEP while the general education teacher handles the rest, for instance, gives both teachers clarity about their respective responsibilities. This clarity matters as much for the teachers' own workflow as it does for the consistency students ultimately experience.

Building a Shared Configuration Both Teachers Understand

Both members of a co-teaching pair should understand how the shared AI grading tool has been configured, including how accommodations are flagged and adjusted, since a special education co-teacher who does not fully understand the tool's configuration cannot effectively review or adjust feedback for students with accommodations. A joint training session, walking through the configuration together rather than training each teacher separately, builds this shared understanding far more effectively than separate, disconnected onboarding. This joint approach also surfaces disagreements about configuration early, before they show up as inconsistent grading in front of students.

• Configure AI grading tools jointly, with both co-teachers contributing their respective expertise from the start
• Assign clear responsibility for reviewing and adjusting feedback for students with specific accommodations
• Train both co-teachers together on the tool's configuration, rather than separately and independently
• Communicate regularly between co-teachers about how accommodations are actually being applied in practice
• Revisit the shared configuration each time a new student with different needs joins the classroom

Supporting the Full Range of Student Needs in One Classroom

Inclusion classrooms typically serve a genuinely wide range of student writing ability and need within a single section, from students working well above grade level to students with significant, documented writing accommodations, which makes rigid, one-size-fits-all AI configuration especially poorly suited to this specific classroom context. Co-teachers should build flexibility into their shared configuration from the start, expecting to adjust feedback tone and rubric weighting for individual students rather than assuming a single configuration serves everyone adequately. This flexibility is not an extra burden, it reflects the actual instructional reality that inclusion classrooms are specifically designed to serve.

The co-teaching model's core strength, two educators bringing complementary expertise to a shared group of students, extends naturally to how AI-assisted grading gets used well in this setting, provided both teachers actively coordinate rather than working in parallel without communication. Schools should recognize that inclusion classrooms may need more, not less, structured support and training time when rolling out an AI grading tool, given the added coordination complexity involved. Treating inclusion classrooms as a standard use case, rather than one with distinct needs, risks a rollout that does not actually serve these classrooms well.

What School Leaders Should Ask Before a Districtwide Rollout

School leaders planning a districtwide AI grading tool rollout should specifically ask how the tool supports co-teaching and inclusion classroom configurations, rather than assuming a standard single-teacher rollout model applies equally well everywhere. A tool that only supports one configured account per class section, with no way for a co-teacher to review or adjust feedback independently, creates real friction for exactly the classrooms that may need the most flexible support. Asking this question during procurement, rather than discovering the gap after rollout, protects inclusion classrooms from being an afterthought in a broader technology decision.

Special education directors should be included directly in AI grading tool procurement conversations from the start, since they bring specific expertise about inclusion classroom needs that a general curriculum or technology team may not have visibility into on their own. This inclusion in the procurement process, mirroring the collaborative model co-teaching itself is built around, produces a tool selection and configuration that genuinely serves every classroom in a district, not just the most straightforward, standard ones. That upfront collaboration pays off directly in how well the tool actually works once it reaches inclusion classrooms.

Learning From Co-Teaching Pairs Who Have Done This Well

Some of the strongest examples of coordinated AI-assisted grading in inclusion classrooms come from co-teaching pairs who have worked together for several years and already have a well-established collaborative rhythm before introducing a new tool into their shared practice. These experienced pairs tend to adapt their configuration approach more smoothly than newly formed co-teaching pairs still building their working relationship, since the underlying trust and communication habits a tool requires are already in place. Schools pairing new co-teaching teams should recognize this and provide additional support during a tool rollout for pairs still establishing their collaborative foundation.

Departments should consider pairing a newer co-teaching team with a more experienced one specifically for AI grading tool onboarding, letting the newer pair observe and ask questions about how an established team has coordinated their configuration and review responsibilities. This kind of structured peer mentorship, similar to the mentoring model that benefits new teachers generally, gives a newer co-teaching pair a concrete example to learn from rather than working out every coordination detail entirely on their own. That peer support tends to shorten the time it takes a new pair to reach the kind of smooth, coordinated practice more experienced teams already have.