Algeria's Ministry of Higher Education used an AI matching platform nationwide for the first time in 2025, placing 97.34% of 340,901 baccalaureate holders and giving 70.3% a top-three choice; a 2026 update adds a 24/7 LLM assistant, but the algorithm's logic remains undisclosed.
340,901
Registered candidates (2025 baccalaureate cohort)
331,827 (97.34%)
Candidates oriented to a university place (2025 baccalaureate cohort)
9,074
Candidates granted extended deadline (extended to 8 August)
70.30%
Placed in one of top three choices
65.30%
Chose science/technology fields
increased from 10 to 12
Preference choices offered (2026 intake)
Details
Maturity
Scaling
Promoter
Ministère de l'Enseignement Supérieur et de la Recherche Scientifique (MESRS), Algeria
Period
Launched 30 April 2024 for the 2024–2025 intake; 2025 results reported August 2025; LLM assistant and 12-choice update for the 2026 intake announced 9 July 2026
Algeria's Ministry of Higher Education and Scientific Research (MESRS) launched a digital, AI-assisted orientation platform on 30 April 2024, under Minister Kamel Baddari, for the 2024-2025 intake.
Objectives
To match new baccalaureate holders to university programmes by analysing each candidate's academic results and stated preferences (vœux) against institutional capacity, described by the ministry as using 'AI techniques and data analysis' in the placement decision for the first time at national scale.
Activities
For the 2026 intake, the ministry expanded the system: the number of preference choices rose from 10 to 12, and a large language model (LLM) assistant was integrated into the platform to answer student questions 24/7; a digital ministerial circular formalising the update was published on 9 July 2026.
Results
For the 2025 cohort, of 340,901 registered candidates, 331,827 (97.34%) were oriented to a university place within the allotted timeframe, with a further 9,074 candidates granted an extended deadline to 8 August. Minister Baddari reported that 70.30% of newly oriented students were placed in one of their top three choices, and that 65.30% chose science and technology fields.
Conclusions
The scale and consistency of the reported figures is corroborated across ministry circulars and Algeria's state and independent press over three consecutive intakes (2024, 2025, 2026), but the matching algorithm's exact weighting logic, any independent audit of placement outcomes, and a formal appeals mechanism remain undisclosed, as does any data-protection framework for the system.
Implementation
Implementation detail (cost, timeline, staffing, conditions for success) is not yet available for this practice.
Commonly funded by
National / regional programmes
Indicative funding routes for practices of this type — always check each programme's current calls and eligibility rules.
Do you run this practice?
Claim it —
verified implementers get a public contact pathway and can propose corrections.
Data sources
Where this practice's information was retrieved from, and when.
Bangladesh's a2i, GiveDirectly, UNDP and UC Berkeley piloted ML poverty-targeting from call-detail records across 106,200 Cox's Bazar households, but a …
Researchers with Indonesia's Ministry of Finance built KemenkeuGPT, a retrieval-augmented LLM trained on 2003–2023 financial data from the Ministry, BPS …