Preparing a job interview in the target language using generative AI, on Moodle.
Develop professional language competences (French or Dutch) through AI-simulated job interview practice, fully integrated into a Moodle sequence. The AI handles volume, immediacy and judgment-free feedback; the trainer focuses on what AI cannot assess: prosody, pragmatics, cultural adaptation.
Coherent oral discourse in a professional formal register
Sector-specific vocabulary relevant to the learner's target field
Structured responses using the situation → action → result framework
Self-correction of syntactic and lexical errors through CEFR-based reflection
An AI-supported language learning scenario for adult job seekers, built around job interview simulation using a Large Language Model, fully integrated in Moodle.
SimuLang is a language learning scenario designed for adult job seekers enrolled in professional training programmes. It addresses a concrete pedagogical challenge: developing oral and written communication skills in a second professional language (French or Dutch) under real workplace constraints, while maximising practice time within a limited number of face-to-face sessions.
The scenario relies on a Large Language Model (LLM) integrated into Moodle to simulate a sector-specific recruiter, providing learners with repeated, judgment-free, immediately corrective practice. The trainer is freed from repetitive linguistic correction and can focus on higher-order competences that AI cannot assess.
Target audience: Adult learners in professional reintegration programmes. Entry level: A2. Target level: B1 (CEFR). Language: French professional or Dutch professional, depending on the learner's target sector.
Language learning objectives at B1 level:
Volume problem solved. A single trainer cannot provide 20 learners with sufficient individual oral practice time. The LLM multiplies practice opportunities without multiplying trainer workload. Each learner can run several simulations per week, at their own pace and outside class hours.
Affective barrier reduced. Adult learners in professional reintegration frequently report anxiety about making mistakes in front of a trainer or peers. The AI creates a low-stakes, private space where errors are corrected without social cost, increasing willingness to take linguistic risks.
Trainer time redirected. By offloading surface-level linguistic correction to the AI, the trainer's intervention time is concentrated on higher-value, non-simulable competences: the subtleties of professional communication that no LLM can currently model reliably, and the human relationship that sustains motivation.
Moodle as the connective tissue. The scenario does not require any external platform. All learner productions, reflections and trainer notes are centralised in Moodle. Transcripts are pushed automatically from the simulation interface to Moodle assignments via an n8n webhook and the native Moodle REST API. As a next step, xAPI statements can feed a Learning Record Store (LRS) for fine-grained competence tracking.
Hallucination risk managed by design. The LLM operates within a narrow, trainer-defined prompt: it plays a recruiter, asks standard interview questions, and corrects language errors. It does not generate complex factual content. Where occasional inaccuracies in linguistic feedback may occur, the trainer's session 4 debrief acts as the human validation layer — the trainer can and should override any erroneous AI correction. The AI is a supervised tool, not an autonomous tutor. The trainer remains the sole pedagogical guarantor.
GDPR by design. The simulation interface relies exclusively on Mistral AI, a European provider processing data within the EU. A Data Processing Agreement is in place. No personal data is stored beyond the session. Learners are informed prior to use. This architecture makes the scenario deployable in institutional contexts without legal friction.
The entire scenario runs on a standard Moodle installation. No specialist plugin is required for the core setup.
Optional: xAPI tracking. If fine-grained competence tracking is required, install the Logstore xAPI plugin from the official Moodle plugin directory. This enables xAPI statements from the simulation interface to be sent to a Learning Record Store (LRS) connected to Moodle, tracking each exchange, error and reformulation as a discrete learning event.
No plugin required for the core scenario. Sessions 1, 3 and 4 run entirely on native Moodle activity types. Session 2 requires only that the Moodle REST API (Web Services) be activated in site administration — a standard configuration step available in all Moodle versions from 3.x onwards.