While AI promises speed, personalization, and efficiency, it also introduces risks that go beyond technical glitches. Assessment is deeply tied to questions of authenticity, culture, and equity — and when these processes are handed to algorithms, important human dimensions can be lost.
Bias and inaccuracy: AI systems are trained on uneven datasets that often privilege dominant languages and norms, leading to unfair scoring of accents, dialects, or culturally valid expressions.
Loss of human nuance: While AI can identify grammar or pronunciation errors, it struggles to recognize creativity, humor, intercultural meaning, or the individuality of learner voice.
Data privacy and surveillance: Mobile AI tools collect voice recordings, writing samples, and learner progress data. Without strong safeguards, this data risks being used commercially rather than educationally.
Over-reliance on AI feedback: Learners may come to trust the algorithm’s corrections without reflecting on why something is right or wrong, reducing opportunities for critical thinking and self-monitoring.
Case Study: Corporate Greed
The video below explores how Duolingo’s recent “AI-first” shift shows up most in how the app feels to use. Learners report that stories and audio lessons seem flatter and more generic, with fewer memorable moments. Some community features now route you to paid AI chats, and familiar tools sit behind new tiers, which can be confusing. The result is more content, delivered faster, but less of the small human touches that made practice feel alive. For our project, this matters because mobile assessment is also an experience. When examples feel canned and feedback sounds the same, learners keep moving but feel less engaged and less connected to the material. The promise of AI is precision and speed. The risk is sameness. Designing around that tension should be part of how we evaluate any mobile tool that uses AI for language learning especially because so much of understanding a language requires a certain level of exposure to the culture associated with that language.
Below you'll find a video that goes into detail of Duolingo's ill-fated attempts to cut costs and streamline development of its lessons:
As AI-driven assessment tools become more common, educators need to look beyond their speed and convenience to consider deeper questions about what is gained — and what may be lost. The prompts below are meant to encourage reflection on the tensions between efficiency and authenticity, access and equity, and the evolving role of teachers in a mobile, AI-supported learning environment:
Authenticity vs. Convenience: Do mobile apps assess real language ability or mostly app-specific performance? How should educators balance efficiency with authenticity?
Feedback Quality: Instant correction is fast, but is “right/wrong + retry” enough to build deep skills? What would meaningful feedback look like on mobile?
Equity and Access: Many AI features sit behind paywalls. Does this reinforce inequities in language learning, or is the free tier still valuable?
Teacher’s Role: If daily assessment happens inside apps, what’s left for the teacher? Should instructors be mentors guiding process, or evaluators verifying proficiency?