GenAI in Language Assessment: 7 Practical Strategies for Teachers

The rise of generative artificial intelligence (GenAI), since the public launch of ChatGPT in Nov 2022, has brought another full-scale revolution to language teaching, learning, and, perhaps most significantly, to assessment. GenAI can quickly generate sophisticated texts, respond to questions, summarize readings, and even provide feedback, translate across languages, and simulate conversations.

These capabilities challenge existing assessment approaches and practices. A take-home writing task, for example, may no longer provide the same evidence of a learner’s independent writing ability that educators once assumed it did, since the learner may ask a chatbot to check their work, or indeed a learner may ask it to write the essay for them. GenAI has not, of course, invented academic dishonesty; learners have long been able to use paper mills or engage in other forms of cheating. What GenAI has changed is the ease, accessibility, and scale at which such practices can happen.

The challenge educators face is not simply whether learners use GenAI, but how and to what degree they use it, and whether the resulting performance still provides credible evidence of the ability the assessment is intended to measure.

Making Sense

This technological disruption in education is not entirely new. Technologies have historically changed how languages are taught and learned, from printed textbooks and audio recordings to computers, the internet, automated writing tools, and now GenAI. What is different this time is the speed, accessibility, and complexity of the new technology.

For example, Selvi calls it “the platformization of English language education,” to describe the use of online platforms powered by GenAI. This raises questions not just about what the technology can do, but also about its impact on teaching, commercialization, and power. Emerging research suggests that educators should neither uncritically embrace GenAI nor reject it outright.

A 2025 review of 77 studies on GenAI in language assessment explores how GenAI technology can be used effectively while also highlighting concerns about its limitations, biases, and ethical implications.

The basic question remains: How can we use GenAI effectively in language assessment? This question makes educators’ language assessment literacy even more important. Recent research emphasizes that language assessment literacy must evolve alongside changes in teaching and assessment practices. As GenAI becomes more common, educators need to understand its implications for how they design, conduct, and evaluate assessments.

Educators don’t need to become GenAI specialists, but here we offer seven educator-facing technology strategies (which we here refer to as TechStrats), both efficiently and critically, in language assessment.

TechStrat 1: Starting With What You’re Assessing

Before designing or revising an assessment, ask yourself “what exactly am I assessing?” A common practice is assessing learners’ academic writing skills with an expository essay. If the goal is to assess independent academic English writing ability, unrestricted GenAI use may interfere with the interpretation of the resulting scores. However, if the goal is to assess professional communication using contemporary tools, competent GenAI use may be a legitimate part of the task.

So, try a construct audit of an existing assessment by completing this statement: “I want this assessment to provide evidence that learners can…” Then examine every permitted resource, such as GenAI tools, dictionaries, grammar checkers, and internet searches, and ask whether it helps learners demonstrate the intended ability or instead performs a substantial part of that ability for them. This small exercise moves the conversation beyond “AI allowed/not allowed” toward the more important question of assessment validity: whether the assessment measures what it is intended to measure.

TechStrat 2: Documenting the Learning Process

GenAI presents particular challenges for assessments that rely heavily on a single polished final product, such as an essay. Educators can redesign such an assignment without abandoning it by collecting three or four pieces of evidence that show the learning process. For instance, a writing assessment can involve prewriting, outlining, a rough draft, and a final version. Learners complete prewriting and outlining in class, submit a rough draft, revise it with permitted technological assistance, and attach a 150-word note identifying two important revisions and explaining why they made them.

The purpose here is not surveillance but to make learning visible by adopting a process approach. Rather than judging only the final product, educators can gain insight into how learners make linguistic decisions, respond to feedback, and revise their communication. GenAI-integrated English assessment can be effective when learners are asked to use technological tools and reflect on their choices rather than treating technology use as invisible.

TechStrat 3: Adding a Verification Moment

A practical way to strengthen the credibility of assessment evidence is to give learners a brief opportunity to demonstrate ownership of their work. After a written assignment, for example, randomly select one or two passages and ask questions orally: Why did you organize this paragraph this way? What does this expression mean here? What was difficult about this section? If you had another day, what would you change?

This interaction isn't meant to be an oral defence, nor is the purpose to catch learners using GenAI. Regardless of how well learners respond, the goal is to gather additional evidence about their decision-making and ownership of their work. A 3-minute conversation, brief classroom meeting, or short written reflection may be sufficient. This step is valuable because both the final product and the learning process can provide useful evidence.

If a learner submits remarkably sophisticated writing but cannot explain its meaning or linguistic choices, the educator has identified a gap in the assessment evidence. This isn't automatically proof of inappropriate GenAI use, but rather an opportunity for formative assessment.

TechStrat 4: Assessing Learners’ Critical Use of GenAI

There is an important distinction between assessing with GenAI and assessing learners’ ability to work critically with GenAI. Consider giving learners a GenAI-generated response and asking them to revise or improve it. An advanced language learner might:

• • identify inappropriate register
  • detect unsupported claims
  • improve organization and cohesion
  • identify language that is unsuitable for the context
  • compare two GenAI-generated versions
  • justify which version communicates more effectively

The assessment then measures learners’ linguistic judgment, not simply their ability to produce text. This matters because GenAI output should not automatically be treated as linguistically or culturally neutral. For example, recent research has raised questions about whether GenAI output favors dominant linguistic norms and adequately represents the linguistic diversity of Global Englishes.

TechStrat 5: Using Multiple Sources of Assessment Evidence

In general, one task or assessment should not carry more interpretive weight than it can reasonably support. For example, for a writing task, combine a take-home assignment that permits GenAI use with some controlled writing in class. If assessing speaking skills, combine a prepared presentation with spontaneous interaction. If focusing on reading, ask learners not only to answer comprehension questions but also, occasionally, to explain how they reached an interpretation.

As in TechStrats 2 and 3, think of assessment evidence in three dimensions: product, process, and performance (i.e., the final result, the strategies used, and the ability to complete authentic tasks). These three dimensions do not need to be weighted equally. Even a small amount of additional evidence can make an assessment decision more defensible. This approach is especially important now that GenAI can produce an impressive product without necessarily demonstrating the learner’s underlying language ability.

TechStrat 6: Conducting a Consistency and Reliability Check

GenAI can enormously support language assessment practices. Before using it to generate assessment materials, select texts, provide oral or written feedback, or evaluate task responses, educators should test it themselves. Give the same GenAI tool the same assessment task several times. Then compare the outputs by asking these questions:

• • Does it apply assessment criteria consistently?
  • Does it overlook errors that matter in the course?
  • Does it identify errors that aren’t actually there?
  • Does it favour a particular variety or style of English?
  • Would I make the same judgment if I assessed the work myself?
  • Would I be comfortable explaining this decision to the learner?

This is a simple consistency and credibility check that any educator can conduct before relying on GenAI for assessment-related decisions.

A recent study in an English for Academic Purposes (EAP) context compared expert educators’ judgments of reading-text suitability with those of automated tools, including ChatGPT. While the automated tools provided useful information about text difficulty, they missed important contextual and linguistic factors that experienced educators recognized. Without human oversight, this could lead to misleading judgments.

Responsible AI frameworks also emphasize fairness, transparency, accountability, privacy, and continuous assessment rather than allowing GenAI to make decisions independently without human supervision. Before uploading learner work to an external GenAI system, educators should also check institution’s requirements for privacy, data protection, intellectual property, and approved GenAI use.

TechStrat 7: Making GenAI Use Transparent

Instead of trying to prevent learners from using GenAI, establish a simple technology-use policy in the course syllabus. For assigned tasks, give students the following prompts to respond to:

1. 1. GenAI Tool(s) used
   2. How I used it (key prompts)
   3. Purpose of use
   4. What I accepted and why
   5. What I rejected or changed and why

This simple disclosure can change transparency into a learning activity rather than just a requirement. More importantly, it gives educators an opportunity to examine an important part of the learning process: how learners make decisions when using GenAI. Did the learner recognize inaccurate feedback? Could they distinguish an effective revision from a more sophisticated-sounding GenAI output? Could they explain why a GenAI suggestion was unsuitable for their specific audience or communicative purpose? This information can also help educators recognize when learners may be becoming overly dependent on GenAI.

Practical Considerations

These TechStrats are adaptable, and their feasibility will depend on class size, workload, institutional resources, and assessment context. For example, using multiple sources of assessment evidence can make learners’ language abilities more visible but also increase educators’ workloads, while individual verification conversations may be difficult to manage in large classes. Educators should therefore adapt these strategies to what is practical in their own teaching contexts.

Moving Forward With GenAI in Language Assessment

Perhaps the greatest misconception is that embracing technology means incorporating GenAI into every assessment. Recent research cautions against incorporating GenAI simply because it is available or has become widely used. GenAI should serve assessment purposes, not determine them.

Sometimes the most valid assessment will still be a conversation, an observed performance, a collaborative writing task, or a carefully designed educator-developed assessment. At other times, allowing learners to use GenAI and assessing how critically and effectively they use it may better represent the communication practices they will encounter outside the classroom.

For now, the goal is neither GenAI-resistant nor GenAI-based assessment. Instead, educators can aim for GenAI-embracing language assessment—assessment that recognizes contemporary technologies while remaining anchored in purpose, validity, fairness, and positive washback.

As GenAI technologies continue to evolve, individual tools will inevitably come and go. Educators cannot redesign their assessment philosophy every time a new tool appears. What they can do is continue developing their language assessment literacy. Top of Form

The GenAI revolution has not made language assessment literacy obsolete; it has made it indispensable.

Acknowledgement: The authors appreciate Prof. Mark Warschauer (University of California, Irvine) and Dr. Houman Mehrabian (University Canada West, Vancouver) for reviewing this post, which informed its revisions.