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Consultancy: AI-Assisted Analysis of Learning Assessment Data

JOB DETAILS

Type of contract : Consultant Contract

Level :  Level 3 - Senior

Hiring Unit : Institute for Statistics (UIS)

Duty Station : Montreal

Work location : Remote

Duration of contract : 4 months

Hiring open to : External candidates

Application deadline (Midnight UTC−4 Time) : 09/10/2026

 

UNESCO Core Values: Commitment to the Organization, Integrity, Respect for Diversity, Professionalism

OVERVIEW

As the custodian agency for SDG 4 indicators, the UNESCO Institute for Statistics (UIS) supports countries in producing, analysing and reporting data on student learning outcomes to inform education policy, planning and monitoring.

 

Many countries have built up substantial learning assessment data through national, regional and international assessments, often with rich contextual information on students, households, schools and teachers. However, this data is often used only to report national averages, proficiency levels or basic group differences. More detailed analysis of learning distributions, inequality, vulnerability, school variation and contextual factors remains limited. Countries often find it difficult to frame policy-relevant questions, use methods that account for the technical features of assessment data, interpret results soundly, and turn them into actionable policy messages.

 

Recent advances in artificial intelligence (AI) make it possible for national analysts to carry out sophisticated analyses without first becoming experts in specific statistical software. One example is estimating standard errors using balanced repeated replication (BRR) and plausible values (PV). Capacity building can therefore focus on which questions to ask, which analyses are appropriate, how to judge whether results are sound, and how to interpret them for policy.

 

UIS is seeking a consultant to lead a demonstration project in one case-study country, to be confirmed, preferably in Southern or Eastern Africa. The consultant will: (a) produce policy-relevant tables, figures and findings on learning quality and equity from the country's assessment data; (b) develop and field-test an updated analytical framework with AI-assisted analytical protocols that analysts in other countries can apply; (c) strengthen the capacity of national analysts and policy officials; and (d) recommend how the approach can be replicated elsewhere. The work builds on two UIS studies by Douglas J. Willms: Learning Divides: Ten Policy Questions about the Performance and Equity of Schools and Schooling Systems (2006) and Learning Divides: Using Data to Inform Educational Policy (2018).

ASSIGNMENTS

Under the overall authority of the Head of the Foresight, Research and Methodological Innovation (FRM) Section, and in close collaboration with national assessment analysts and policy officials in the case-study country, the consultant will carry out the assignment in three stages. This structure is indicative. Candidates will propose their own detailed methodology and work plan.

Preparatory stage:

  • Consult UIS and national counterparts to confirm the country, dataset(s) and participants. Prepare an inception note with the analytical plan and work plan.
  • Review the quality, documentation and limitations of the dataset, including its sample design, and identify which analyses are feasible.
  • Work with national analysts to identify priority policy questions and the analyses most useful for informing education policy.
  • Develop and test AI-assisted analytical protocols that let analysts carry out and reproduce the priority analyses with their own data. These must include methods that account for complex sample design and plausible values, such as BRR/PV standard errors.

Long Description

In-country mission:

  • Work intensively with a small group of national analysts for approximately four days.
  • Meet senior policy officials to examine the findings, their implications and the policy questions they raise.

Consolidation and finalization:

  • Prepare a policy-relevant country report with statistical tables, figures and findings for national planning and decision-making.
  • Draft the updated analytical framework as a UIS publication, using the case study and the results of the field test.
  • Prepare a final report covering lessons learned, an assessment of the demonstration, and recommendations for replicating the approach in other countries.

CONTRACT DURATION

The contract will run for approximately four months, from mid-October 2026 to mid-February 2027, with an estimated workload of no more than 20 working days. The fee will be calculated on the basis of actual working days, up to a maximum of 20 days. It is structured around three stages: preparatory work, an in-country mission, and consolidation and finalization. The in-country mission is expected to take place in late November or early December 2026; exact dates will be agreed with UIS and national counterparts.

DELIVERABLES

  1. Inception note confirming the country, dataset and participants, and setting out the analytical plan, work plan, and the AI tools and methods to be used – by 6 November 2026. 
  2. AI-assisted analytical protocols, tested and documented, with guidance on which questions to ask, appropriate analyses, how to check the soundness of results, and how to interpret them for policy – by 22 January 2027, following the in-country mission. 
  3. Country report with statistical tables, figures and findings on learning quality and equity. It will include a summary of the policy implications and questions raised with senior policy officials – by 22 January 2027. 
  4. Final manuscript of the updated analytical framework, for publication by UIS, which builds on the two Learning Divides publications, uses the case-study country as illustration, and incorporates the AI-assisted protocols – by 12 February 2027. 
  5. Final report summarizing activities, methods, field-test findings and lessons learned, with an assessment of the demonstration and recommendations for replication in other countries – by 12 February 2027.

Editing, layout and publication of the analytical framework will be handled by UIS after the contract ends.

Payment will be linked to the satisfactory delivery and acceptance of the deliverables, in accordance with UNESCO procedures, in three instalments:

  • 20% upon acceptance of the inception note (Deliverable 1); 
  • 40% upon acceptance of the AI-assisted analytical protocols and the country report (Deliverables 2 and 3); 
  • 40% upon acceptance of the analytical framework manuscript and the final report (Deliverables 4 and 5). 

OWNERSHIP AND AUTHORSHIP

All outputs produced under this contract, including the analytical framework, protocols, code and reports, are the property of UNESCO. The consultant will be credited as author of the analytical framework publication in accordance with UIS publication policy, and the contributions of national analysts will be acknowledged. The consultant may not publish, reproduce or use the outputs or the assessment data for any other purpose without UNESCO's written authorization.

Long Description

TRAVEL

The consultancy requires international travel to the case-study country for the in-country mission. Travel arrangements will be agreed with UIS in advance and made in accordance with UNESCO rules and procedures. Travel costs will be agreed as a separate lump sum, distinct from the fee, and may be paid in advance.

Before travelling, the consultant must complete the UN BSAFE security awareness training and obtain security clearance through the UNDSS Travel Request Information Process (TRIP).

COMPETENCIES - Core (C) & Managerial (M)

  • Communication (C)
  • Accountability (C)
  • Innovation (C)
  • Knowlegde sharing and continuous improvement (C)
  • Planning and organizing (C)
  • Results focus (C)
  • Teamwork (C)

 

For detailed information, please consult the UNESCO Competency Framework.

REQUIRED QUALIFICATIONS

EDUCATION

  • A PhD or equivalent advanced degree in statistics, econometrics, psychometrics, education or a related field; OR a Master's degree in a related field. A PhD is desirable.

WORK EXPERIENCE

  • A minimum of ten (10) years of relevant professional experience in statistics, psychometrics, education data analysis or a related field.
  • Demonstrated expertise in analysing large-scale learning assessment data, including international or regional learning assessment programmes.
  • Senior-level experience in multilevel modelling and complex survey data analysis, including estimation using replicate weights and plausible values.
  • Demonstrated experience in policy-oriented analysis of learning assessment data.
  • Experience using, or the ability to develop, AI-assisted and reproducible analytical workflows.
  • Proven experience in capacity building and training delivery, including work with national analysts and senior policy officials. This must be demonstrated by at least five (5) years of experience or three (3) comparable assignments, with references available upon request.

Long Description

SKILLS AND COMPETENCIES

  • Proficiency in at least one statistical software package (e.g. HLM, R, Stata), sufficient to verify the results of AI-assisted analyses.
  • Ability to design reproducible analytical protocols and to document the responsible use of AI tools in line with data-protection requirements.
  • Excellent drafting skills, with proven ability to produce technical reports, publications and policy-oriented outputs for diverse audiences.
  • Ability to work independently in a remote setting and meet deadlines.

LANGUAGES

  • Excellent proficiency in English (written and spoken) is required.
  • Working knowledge of French is an asset.

APPLICATION PROCESS

Interested candidates should complete the online application, then download and complete the Employment History form (Word file). At the end of the Word file, insert extra pages with the following required information:

Part 1: Technical Proposal

  • An up-to-date curriculum vitae;
  • A statement of understanding of the assignment, indicating how the candidate's qualifications and experience make them suitable;
  • The proposed methodology and detailed work plan, including the use of AI-assisted analytical protocols, the proposed duration and allocation of the in-country mission, a timeline with milestones consistent with completion by mid-February 2027, and any inputs required from UNESCO;
  • Examples of similar work, with at least two (2) references from comparable assignments.

Part 2: Financial Proposal

  • Daily rate and estimated number of working days (not exceeding 20), quoted in a single currency (EUR/USD/CAD/GBP). The fee will be calculated on the basis of actual working days, up to a maximum of 20 days, at a daily rate within the applicable UNESCO scale for Senior consultants;
  • Estimated travel costs and related expenses for the in-country mission (airfare, accommodation and daily subsistence allowance, in accordance with UN rates);
  • Any other relevant cost elements.

Only complete applications received by the deadline will be considered. Responses to the mandatory pre-screening questions form an integral part of the application; candidates who do not meet the eliminatory criteria will not be considered further. Only shortlisted candidates will be contacted.

SELECTION AND RECRUITMENT PROCESS

Please note that all candidates must complete an online application and provide complete and accurate information, by the above deadline.

To apply, please visit the UNESCO Careers website. No modifications can be made to the application once submitted.

The process may include pre-recorded video interviews and/or written assessments, interviews with a Panel, as well as reference checks. In addition, candidates may be requested to provide additional information which may be pertinent to the position's qualifications.

Please note that all candidates, whether selected or not, will be informed of the outcome of their application in due course.

Short-listed candidates may also be added to Talent Pools, subject to their consent (i.e. Data Privacy Statement).

ADDITIONAL INFORMATION

  • UNESCO recalls that paramount consideration in the appointment of personnel shall be the necessity of securing the highest standards of efficiency, technical competence and integrity.
  • UNESCO applies a zero-tolerance policy against all forms of harassment.
  • Individuals from minority groups and indigenous groups and persons with disabilities are equally encouraged to apply.
  • All applications will be treated with the highest level of confidentiality.
  • UNESCO does not charge a fee at any stage of the hiring process.

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