Evaluating AI Language Tutors Through a Security Lens

Evaluating AI Language Tutors Through a Security Lens
Over the past year or so, I have seen a noticeable increase in AI-driven language learning tools.  They are advertised as convenient, always-available tutors that can help you practice conversation in a low-pressure setting.  As someone with a moderate level of proficiency in a couple languages, and extended exposure to several others, I've always been interested in language learning methodologies. When these AI tutors started gaining popularity, I was curious how effective they actually were, so I decided to test a few of the more popular options and read through a number of user reviews to get a better feel for how they worked in practice.
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During this review, something started to stand out.  Mixed in with the normal small-talk prompts were questions that looked a lot like the same kinds of things we use for “security questions” during account recovery.  The conversations themselves were framed as innocent language practice, but from a security standpoint it is important to temporarily ignore the expected use case and ask a different question: if this same functionality were misused or abused, what else could it be used for?

Setting the Stage
To put this in context, consider a typical session with an AI language tutor.  You log into the app, select a scenario, and start a chat session that is presented as a private, one-on-one conversation.  The AI might ask about your family, your pets, where you grew up, or your favorite hobbies.  All of that makes sense from a language learning perspective, and on the surface nothing appears malicious or out of place.

Now contrast that with a similar exercise in a traditional classroom.  An instructor might ask the same kinds of questions as part of a role-play, but the answers are spoken aloud, not stored, and the interaction is limited to the people in the room.  There is no persistent transcript, no automatic aggregation across thousands or millions of learners, and no centralized system correlating the information you share with other data sources.

Where the Security Questions Start to Overlap
The problem is that many of the “get to know you” questions used to drive natural conversations are the same kinds of questions that show up as knowledge-based authentication prompts.  Things like a first pet’s name, a birth month, a hometown, or a favorite color are commonly used as backup authentication factors.  On their own, none of these answers feel particularly sensitive, especially when they are asked over the course of multiple sessions and wrapped in the context of language practice.

From an attacker’s perspective, though, these details can be extremely valuable.  AI language tools are designed to collect and analyze conversation data at scale, across very large user populations.  If any of that data is exposed (through a breach, overly broad access, or misuse by a third party) it can be combined with existing public information to build highly targeted phishing or account recovery attacks.  Even without a direct compromise, large-scale analysis can reveal common patterns in how people answer certain questions, making probabilistic guessing of weak security questions more effective.

This risk compounds because most AI tutors require user accounts for personalization and progress tracking, directly associating security-question-like answers with your email, username, or device ID.  A leak would not just spill generic data, it maps answers to identifiable profiles, streamlining targeted attacks.

Understanding the Risk Without the Hype
It is important to be clear about what this does and does not mean.  The presence of these questions in AI language tools does not automatically imply malicious intent.  In most cases, the goal really is to improve the quality of the learning experience by making conversations feel more natural and personalized.  These tools can be very effective, and there is no need to overstate the risk in order to justify reasonable precautions.

At the same time, the fact that the conversations are “innocent” from a user’s point of view does not remove the underlying security implications.  Any environment that collects potentially sensitive personal information needs to be evaluated as a possible source of data for phishing and other attacks.  This is especially true when collection happens at scale and data is stored in centralized systems.  Treating these tools like any other data-collecting application is a good starting point for understanding and managing the risk.
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Practical Considerations for Safer Use
From a user standpoint, there are a few straightforward steps that can reduce the likelihood that this kind of data will be useful in an attack.  One effective approach is to create a consistent fictional persona specifically for use with AI language tutors.  This means developing a set of alternative answers for common personal questions (a different pet name, hometown, birth month, or family details) that you use exclusively in these contexts.  The key is consistency: using the same fictional details across sessions maintains the personalization benefits of the platform while ensuring that any compromised data points to a non-existent person rather than your actual security question answers.  Beyond the persona itself, favoring tools that clearly document how conversational data is stored, who can access it, and how long it is retained provides additional insight into the actual risk profile.

On the authentication side, reducing reliance on traditional security questions is still a good idea.  Using stronger primary authentication methods and avoiding easily discoverable or guessable backup questions lowers the value of any data that might be collected through these tools.  Even if the language platform itself is never compromised, these changes make it more difficult for attackers to leverage incidental personal information gathered from any source.

For those interested in a structured approach to building this fictional persona, there's a straightforward method outlined at the end of this post.

​Wrapping It Up
The main takeaway here is not that AI language tutors are inherently unsafe, but that they should be evaluated with the same level of scrutiny as any other application that collects and stores user data.  The same conversational prompts that make the experience feel natural can also map closely to knowledge-based authentication questions in other systems.  As with any technology, there is a balance: they can be both useful and potentially risky at the same time.

It is also worth remembering that many of these concerns around security questions can be sidestepped entirely by moving to multi-factor authentication (MFA) wherever it is available.  Current security guidance from NIST and CISA recommends phasing out knowledge-based authentication in favor of MFA methods like authenticator apps, hardware tokens, or passkeys.  These approaches do not rely on personal information that could be collected through conversation, making the data gathered by language tools far less useful to attackers.  Where you have control over your authentication methods, choosing MFA over security questions is one of the most effective ways to reduce this particular risk.

Ultimately, it is still the responsibility of each user to maintain awareness of what they are sharing and how that information could be used in a different context.  By treating “innocent” conversations as data that may persist, and by making a few adjustments to both how we answer and how we design our own authentication schemes, we can continue to benefit from these tools while reducing their value as a source of information for phishing and related attacks.

Bonus: A Quick Tool for Building Your Fictional Persona
If you want to streamline the process of creating a consistent fictional persona, you can use an AI assistant to help generate and track your alternative answers. The prompt below instructs the AI to build up a list of persona details incrementally as you ask questions, maintaining consistency across all your responses. If you don't like a particular answer, you can ask it to regenerate that specific detail while keeping everything else intact.

The approach is straightforward: paste the prompt into any AI chat interface, then pose questions as if you were the AI language tutor asking them ("What is my first pet's name?", "Where did I grow up?", etc.). The AI generates an aTnswer and maintains your growing persona list. Type "retry" if you want a different answer for the most recent question. Once you have a complete set of details you're comfortable with, save them somewhere secure for reference during your language practice sessions.

Prompt