The best AI assistants for teachers: from lesson preparation to useful feedback

After a working day, a teacher opens a chat and requests a lesson. A minute later, objectives, exercises, a test, and homework appear. The document looks finished, yet reading raises questions: does it suit the group, fit available time, and assess the intended skill? Savings begin only after these questions are answered.

Teachers should link AI selection to recurring actions: creating examples, clarifying instructions, adapting materials, or editing feedback. This review offers selection scenarios based on official service capabilities. Its hypothetical original cases demonstrate methods rather than measured superiority of any product.

First Find a Routine You Can Describe

‘Prepare a good lesson’ is too broad for an initial trial. Narrow it: create two counterexamples, check instructions for clarity, propose a scaffolded exercise, or shorten feedback without losing substance. These actions have visible results and clear acceptance criteria.

Record how you spend time over a week. Some work needs teaching decisions; other work concerns presentation. Identifying why students struggle is harder than rephrasing an existing explanation. Begin delegating where quality can be checked quickly.

UNESCO's AI competency framework for teachers treats knowledge, pedagogical use, and responsibility as connected elements. For selection, the practical conclusion is simple: access to tools should come with an ability to evaluate results in specific learning contexts.

ChatGPT: A Flexible Partner for Developing Exercises

Try ChatGPT for example variants, instruction analysis, and simulated learning dialogues. The capabilities overview describes general product tools; check availability in your account. Clear tasks matter more for lesson preparation than lengthy feature lists.

Provide a learning outcome: ‘Students select a metric for comparing differently sized groups.’ Request three short situations where absolute counts and proportions create different impressions. Check each independently. Tasks should require metric selection rather than accidentally reduce to mechanical division.

Ask where instructions force students to guess. Is the response format named? Are all data present? Is assessment explained? This editorial view may help even when lesson content is ready and no new generation is needed.

Claude: Editing Arguments and Learning Texts

Include Claude in trials if methodological texts, long instructions, and written-work feedback constitute much of your work. Official Claude for Education materials describe educational scenarios. For specific files, check current upload and access conditions.

Suppose an instructor prepared an explanation of causal and statistical relationships. Ask where examples permit misinterpretation and request one clarification. Automatic rewriting of entire sections is unnecessary. Preserving disciplinary terminology and logic matters more than a new style.

For student feedback, supply your own subject observations and request concrete, consistent wording. Assistants can help with form, but teachers must assess evidence and whether criteria were met. Automatically polite text without substantive support gives students no next step.

Gemini: Materials and Tools in Google's Educational Environment

Institutions using Google Workspace should check Gemini for Education and available Classroom tools. Official pages describe educational capabilities, but actual sets depend on institutional conditions and settings. Personal profiles should not be treated as full equivalents of educational environments.

Trial tasks can combine text, tables, and diagrams. Request questions helping students connect these materials, then check whether they require knowledge not yet taught. Convenient file access helps only when content is read correctly and used appropriately.

Check Ukrainian terminology separately. Adapting complexity may replace precise concepts with everyday words. Sometimes this helps; sometimes it changes meaning. Retain a short course glossary and request explicit flags where simplification needs your decision.

Copilot and Teach: Preparing Materials in Microsoft

Microsoft describes Teach in Copilot as an environment for preparing materials including plans, tests, and rubrics. Teachers already using Microsoft have reason to test complete workflows. Confirm tool availability and permissions within your institution.

Begin with a rubric for a specific assignment. Supply learning outcomes and request observable features for three performance levels. Remove vague words such as ‘good’ or ‘sufficient’ where they do not explain student behavior. Rubrics should support consistent decisions rather than merely look attractive in tables.

Apply it to two hypothetical assignments. If almost identical results receive different grades or criteria duplicate each other, refine the rubric. Generation is the first stage. Pedagogical suitability emerges when applied to specific material.

DeepSeek and Grok: Additional Roles in Teachers' Toolkits

Test DeepSeek on code, mathematical transitions, and counterexamples. Test Grok for context around public discussions, as described in its official FAQ. These are possible supplementary roles, not reasons to transfer all course preparation into several new services simultaneously.

For technical subjects, request boundary cases for learning algorithms. For media literacy, trace the origins of one prominent claim. Teachers independently verify both. Search summaries do not replace primary-source reading, and generated tests need execution or manual analysis.

Do not expand toolkits solely because products are popular. If transferring context takes more time than tasks save, another service may be unnecessary. Know clearly why each tool belongs in the workflow.

Case: One Learning Outcome, Three Levels of Support

Suppose students need to distinguish facts from interpretation in short messages. One group receives marked passages; another the same text unmarked; a third two contradictory accounts and a request to reconstruct verifiable foundations. The objective remains shared while support varies.

AI can create variants, but teachers must verify they train the same skill. Simplification should not remove the act of choosing; greater difficulty should not merely mean longer sentences. Identify exactly what students do in each case.

Provide conditions without real students' personal characteristics. Descriptions of learning needs suffice: more scaffolding, shorter texts, or more complex context. Private diagnostic data are unnecessary. Educational adaptation can begin with functional task descriptions.

Case: A Test That Actually Checks Understanding

A teacher requests ten questions after a percentages lesson. Assistants may produce simple calculations even though the objective is distinguishing percentage changes from percentage points. Specify student actions rather than general topics to avoid this.

Request two recognition questions, two explanation questions, and one error-finding question. Each needs a correct answer with brief justification. Check that closed questions do not have multiple correct options and that answer length does not reveal the key.

Then take the test yourself as a student. Can answers be guessed without knowledge? Are unstated assumptions needed? Does difficulty fit available time? Five verified questions often help more than large automatic banks nobody read carefully.

Feedback: Specificity Instead of General Praise

‘Good work, but more analysis is needed’ barely explains what to do next. Teachers first identify subject problems: unsupported conclusions, missing conditions, or sources compared against different criteria. AI can then help express feedback briefly and clearly.

Useful structure includes observation, consequence, and next action. For example: paragraph two names two findings without shared criteria; readers cannot see grounds for comparison; add a criterion and revisit both sources. This describes the work rather than judges students' personalities.

Do not let models invent motivation: ‘you did not try’ or ‘you did not understand the topic’ does not automatically follow from errors. Feedback should address observable work. In individual conversations, teachers can ask clarifying questions that context-free chats cannot replace.

Assessing Time Savings Honestly

Choose a recurring task and measure total normal preparation time. Then use AI in a comparable case. Include checking, correction, and transfer into required formats. Otherwise brief generation can appear to save substantial time while overall work increases.

Assess quality too. Instructions reducing student clarification questions are useful even if preparation takes the same time. A longer but no more accurate test gains no advantage from volume. Define success metrics before trials.

Collect representative examples for colleagues: inputs, outputs, and corrections. Small pilots need not prove effectiveness institution-wide. They help identify workable conditions and where team support is needed.

Course Organization and Usage Rules

For WeStudy course materials, explain permitted AI roles and expected outcomes alongside assignments. Students should know whether hints, editing, or drafting are allowed. General statements about responsible use do not always support decisions in particular situations.

Teachers also need data, access, and version-management rules. Organizational accounts may differ from personal ones. Before scaling, test workflows through ordinary participant profiles: administrators often see more features than students or colleagues.

The WeStudy LMS platform can support learning sequences while AI supports individual preparation actions. Final materials should remain available in ordinary formats. Courses can then be edited, tools changed, and decisions explained without dependence on one private chat.

From Learning Outcomes to Verifiable Tasks

Imagine a teacher preparing an argumentation lesson. ‘Students will understand persuasive texts’ is too broad to assess. Before consulting assistants, she refines the goal: distinguish claims, evidence, and assumptions in short paragraphs. Services can now create three learning texts with different structures. Each should support this action rather than merely be interesting to read.

The first text might have a clear claim and relevant example; the second might substitute emotional judgment for evidence; the third might hide an assumption. The teacher reads them without prepared explanations and completes the task herself. Three extra complex problems in the second text may overload beginners. Multiple justified answers in the third must be incorporated into criteria or addressed through revised wording.

After checking materials, assistants may suggest a discussion sequence: individual choice, pair comparison, and shared analysis. Stage durations depend on groups and real lesson conditions. Treat generated plans as drafts for professional judgment. Teachers know where students usually stop, which examples they have seen, and how long discussion takes in particular classrooms.

Checking Incorrect Test Options

Poor tests often reveal correct answers through form. They are longer, more precise, or fit question grammar better, while distractors look random. Ask assistants specifically to check consistency of length, structure, and detail rather than solve tests. Such editing helps after subject verification: neat presentation does not guarantee substantive quality.

For average-value questions, build distractors around typical student actions: dividing sums by incorrect counts, selecting maximum values, or confusing means with medians. Teachers should understand the error behind each option. If explanations of errors must be invented after generation, options may not serve diagnostic purposes. Random numbers merely create the appearance of choice.

Check for second correct answers under other reasonable interpretations. ‘Always,’ ‘best,’ and ‘correct’ often conceal unstated restrictions. Ask assistants to find interpretations making keys ambiguous, but leave final decisions to subject experts. After use, real student responses reveal more than prior difficulty assumptions.

Assessment Rubrics Should Work on Concrete Examples

Attractive rubric labels such as ‘excellent,’ ‘sufficient,’ and ‘partial’ do not explain levels. In oral presentations, ‘argument quality’ can involve observable features: a stated claim, evidence connected to it, and consideration of substantial objections. Assistants help remove repetition and vague adjectives, but task goals and course levels determine criteria's substance.

Begin testing rubrics on two or three hypothetical assignments. Teachers assess them and identify uncertainty. Strong evidence combined with an unclear claim should produce an explainable decision rather than guesses at overall impressions. Provide fictional examples without personal data and ask which criterion wording permits conflicting interpretations.

Read rubrics through students' eyes before group use. Is improvement clear? Do two criteria penalize the same weakness? Does visual presentation receive disproportionate weight in source-analysis tasks? These questions preserve alignment with outcomes. Generation speeds preparation, while testing examples makes text usable.

Feedback without Losing the Teacher's Voice

Imagine a teacher’s brief essay notes: “claim present; second example does not support the conclusion; source unexplained.” The assistant can turn them into a coherent comment if instructed not to add new evaluative judgments. Supply only verified observations and the desired length. After editing, the teacher compares every sentence with the student’s work so that a friendly tone does not conceal invented praise or criticism.

Useful comments identify the problem's location, consequence, and next action: ‘In paragraph two, the example shows service popularity, but your claim concerns learning quality. Add evidence measuring learning outcomes or narrow the conclusion.’ Long introductions are unnecessary. Students can return to specific passages and make verifiable changes.

When commenting at scale, watch repeated template phrases. If everyone is told to ‘deepen analysis,’ nobody learns their next step. Assistants can shorten or simplify language, but individualized feedback comes from teachers' precise observations. Preserve these even when editing makes comments smoother.

A Small Pilot before Routine Use

Begin new tools with one recurring task, such as three seminar exercise variants. Over several lessons, record drafting, checking, and post-use correction time. Separately record whether tasks revealed intended errors. If editing eliminates savings, revise the scenario even when initial text appears instantly.

Teaching teams can agree to mark verified versions, subject reviewers, and use dates so colleagues avoid accidental drafts. Retain the ability to work without particular services: materials and criteria should remain in familiar course environments. Tools then support teaching without access changes stopping lessons.

Discussing AI Use with Students

Briefly explain where assistants helped prepare lessons. For example, they suggested initial examples, then teachers checked content, changed conditions, and defined assessment criteria. This demonstrates actual responsibility allocation and models how students can describe their own tool use without exaggerating its contribution.

Discuss a rejected example and why it was discarded. Perhaps conditions lacked data, distractors were too obvious, or explanations mixed concepts. Concrete draft analysis makes checking visible. Students see that finished learning materials follow professional selection rather than merely a button press.

Lessons should not become service advertisements. The main question is which features make tasks good and how to check them. If students identify necessary conditions, notice ambiguity, and explain answer criteria, AI discussion supports subject learning. Particular tools remain workflow examples reproducible through other means.

End discussion by returning to and completing the assignment. This reinforces the connection between tool analysis and the knowledge motivating the lesson.

Choices That Preserve Teaching Attention

The best teacher assistants free attention for human work: noticing misunderstanding, changing explanations, discussing arguments, and providing timely feedback. Entire courses need not be automated. A few well-tested scenarios with clear boundaries suffice.

Start with one task, give tools specific context, and check results against actual material. If benefits recur, describe the method for yourself and colleagues. Otherwise change the task or remove unnecessary steps. Professional judgment lies in precisely this choice.

Sources and Further Reading

• AI competency framework for teachers

  
  

• capabilities overview

  
  

• Claude for Education

  
  

• Gemini for Education

  
  

• Teach in Copilot

  
  

• official FAQ

  
  

• UNESCO: Guidance for generative AI in education and research — further reading