Sébastien Van Drooghenbroeck Moot Court Competition on the European Social Charter

REGISTRATION FOR THE 2026 COMPETITION

Applications should be submitted using the form available here

https://docs.google.com/forms/d/e/1FAIpQLSe5sJlEAkouJJYXERy5_8AuWeb3NsTHCYqRaWNBBlY4lbqFRA/viewform?usp=sf_link

COMPETITION TIMELINE 2026

• 20 October 2025, h. 23,59 CET – deadline for enrolling in the competition by sending the completed form available on ANESC site at mootcourt2026@racse-anesc.org

• 17 November 2025, h. 23,59 CET – ANESC Secretariat communicates the Moot case to the teams that are properly enrolled in the competition, together with their ‘legal standing’ ((by drawing lots, according to Art. 2.3. of the Rules)

• 1 December 2025, h. 23,59 CET – deadline for submission of questions to the Scientific Committee of the Competition at mootcourt2026@racse-anesc.org (max. 2 questions addressed by each team to the SC, if the case, according to Art. 5.4. of the Rules)

• 9 December 2025 – sending the answers to the teams by the Scientific Committee

• 9 March 2026, h. 23,59 CET – submission of complaints/memorandums at mootcourt2026@racse-anesc.org

• 16 April 2026– communication of the papers to the opposite teams by ANESC Secretariat, according to Art. 5.6. of the Rules (7 days before the oral phase)

• 24 April 2026 – NEW Oral Phase (UCLouvain Saint-Louis, Brussels) Agenda and practical information are available at the following link: https://www.circ.usaintlouis.be/moot-court-competition-on-the-european-social-charter/

RULES

Download the rules here.

CASE

Download the case here.

TEAMS

(In the order of their enrollment)

Eötvös Loránd University Budapest (Hungary)
Jean Moulin University Lyon 3 (France)
University of Rouen Normandy (France)
Free University of Tbilisi (Georgia)
Vrije Universiteit Brussel (Belgium)
UCLouvain Saint-Louis Brussels (Belgium)
University of Strasbourg (France)
Marie Curie-Sklodowska University Lublin (Poland)

SCIENTIFIC COMMITTEE

Nino CHITASHVILI (CoE’s Department of Social Rights)
Padraic KENNA (ANESC)
Giuseppe PALMISANO (former member of the European Committee of Social Rights)
Catarina SANTOS BOTELHO (ANESC)
Laura SPATARU NEGURA (ANESC)
George THEODOSIS (Vice-President of the European Committee of Social Rights)
Amaya UBEDA (CoE’s Department of Social Rights)

QUESTIONS & ANSWERS

Eötvös Loránd University
Question 1
What specific analytical methods are employed by the AI-based monitoring technologies
mandated by the Victherian Government, and which form of human oversight is
incorporated into the evaluation of the resulting analyses?
Under the 2025 Act, each long-term care facility uses a unified AI-managed monitoring
system. Data processing works as follows:
1. Data collection
• Sensors: movement, bed-exit, door activity, fall-detection signals.
• Wearables: heart rate, temperature, location inside the facility.
• Video in common areas: presence, behaviour patterns, incidents; facial
recognition is sometimes used.
2. Local processing
All data flows into an on-site server that:
• cleans and time-stamps the data;
• classifies events (e.g., “fall”, “restless movement”, “wandering”);
• generates alerts for staff;
• forwards information to the central AI engine.
Video is processed almost immediately to extract behavioural information, but recordings
are kept locally for about 48 hours.
3. AI analysis
The central AI system:
• produces risk scores for each resident;
• detects changes in behaviour or health patterns;
• determines what alerts appear on staff dashboards;
• feeds into staff performance scoring (e.g., response times, unattended alerts).
4. Storage
• Local storage:
◦ video: ~48 hours
◦ sensor/wearable raw data: ~30 days
◦ AI-generated alerts: ~6 months
• Central storage: aggregated metrics and analytics kept for 1–3 years.
5. Access
• Care staff: real-time alerts + limited history
• Nurses: full monitoring history
• Facility management: all data + performance dashboards
• Residents/families: only general summaries (consent considered inadequate)
Human oversight
1. 2. 3. 4. Care staff review real-time alerts, verify false positives, and escalate emergencies.
Nurses/supervisors check incident logs and risk scores and may override AI
classifications.
Facility management audits dashboards and performance data from a managerial
standpoint.
Vendors conduct technical maintenance and accuracy checks but do not make
care decisions.

Question 2
In the case of employees arriving from third party states, does Vichtery recognize existing
professional qualifications as a condition for permanent residency status , and does it
require them to pass a language exam within 1-2 years in order to extend that same
residence permit?
Vichtery allows third-country care workers to enter with provisional recognition of their
qualifications, valid for up to two years. Full recognition requires a short bridging course
and an assessment. To renew their residence permit, workers must pass a B1 language
exam within 1–2 years. Permanent residency becomes available after five years of legal
residence, but only if the worker has full qualification recognition, stable income, and has
met the language requirement.

UMCS-Lublin
Question 1
Is there in Vichtery (beyond the LTCTDOA Act itself) a specific national law, standard, or
implementing regulation that defines the minimum requirements for informed and
voluntary consent from LTC residents (or their legal guardians) for the mandatory use of
AI monitoring technologies (including facial recognition/behavioural analysis), specifically
concerning the right to object or data retention policies?
There is no national legal instrument – outside the 2025 Act – defining consent standards or
data-protection requirements for mandatory AI monitoring in Vichtery.

Question 2
In relation to the eviction order issued on July 6, 2025, concerning “Bluewood Gardens,”
was a formal, external technical expertise (structural assessment) prepared and
publicly released that specifically confirmed the existence of “urgent safety concerns” as
the sole basis for the order, or was the eviction decision based exclusively on an internal
administrative memo and coordination with the Ministry of Innovation?
The eviction order was not supported by any externally validated structural-safety analysis.
Instead, it seems to have been grounded solely in internal administrative reasoning,
strongly influenced by concerns about the Digital Care Innovation Centre project.

University of Strasbourg
Question 1
In order to determine the extent of the applicant’s rights, could you clarify the definition and
scope of long-term care provided for by Vichterian law, as well as the benefits actually
covered?
Under Vichterian law, long-term care includes residential care, community-based home
support, and day-care services for older persons who cannot perform essential daily
activities independently. Before the 2025 reform, benefits covered part of residential-care
fees, home nursing, physiotherapy, assistive devices, and means-tested subsidies for low-
income individuals. Municipal top-ups supplemented these benefits. After the 2025 Act,
access became strictly means-tested, staffing standards were suspended, and coverage
for many individuals effectively declined.

Question 2
Does the major reform announced apply uniformly to public and private healthcare
institutions, or does the text make a distinction between them?
The 2025 Long-Term Care Transformation and Digital Oversight Act applies uniformly to all
long-term care institutions, with no distinction between public and private facilities.

University of Rouen Normandy
Question 1
What are the criteria for the evaluation of performance used by the algorithmic system that
applies to workers in the LTC facilities?
Under the 2025 Act, each long-term care facility uses a unified AI-managed monitoring
system. Data processing works as follows:
1. Data collection
• Sensors: movement, bed-exit, door activity, fall-detection signals.
• Wearables: heart rate, temperature, location inside the facility.
• Video in common areas: presence, behaviour patterns, incidents; facial
recognition is sometimes used.
2. Local processing
All data flows into an on-site server that:
• cleans and time-stamps the data;
• classifies events (e.g., “fall”, “restless movement”, “wandering”);
• generates alerts for staff;
• forwards information to the central AI engine.
Video is processed almost immediately to extract behavioural information, but recordings
are kept locally for about 48 hours.
3. AI analysis
The central AI system:
• produces risk scores for each resident;
• detects changes in behaviour or health patterns;
• determines what alerts appear on staff dashboards;
• feeds into staff performance scoring (e.g., response times, unattended alerts).
4. Storage
• Local storage:
◦ video: ~48 hours
◦ sensor/wearable raw data: ~30 days
◦ AI-generated alerts: ~6 months
• Central storage: aggregated metrics and analytics kept for 1–3 years.
5. Access
• Care staff: real-time alerts + limited history
• Nurses: full monitoring history
• Facility management: all data + performance dashboards
• Residents/families: only general summaries (consent considered inadequate)
The function of the algorithmic system is highly technical, but there is human oversight of
worker performance as part of the audit and evaluation process for each AI system used
(in accordance with the Framework Convention on Human Resources and AI).

Question 2
What are the rights guaranteed to a worker subject to an evaluation system of algorithmic
performance concerning the decision for their dismissal in Vichterian law?
Under Vichterian law as described in the case, a worker subject to algorithmic
performance evaluation has, in principle, the general labour-law right not to be dismissed
without human supervision, valid grounds, and access to an appeal mechanism. These
guarantees derive from ordinary dismissal protections and from Vichtery’s commitments
under Article 24 RESC. However, the LTCTDOA does not provide any specific rights
concerning algorithmic evaluation itself.

Vrije Universiteit Brussel
Question 1
What were the minimum staffing ratios in Vichtery prior to 2023, how many care worker
positions remained unfilled, and what was the average duration of waiting lists for elderly
individuals seeking long-term care?
Under the 2025 Act, each long-term care facility uses a unified AI-managed monitoring
system. Data processing works as follows:
1. Data collection
• Sensors: movement, bed-exit, door activity, fall-detection signals.
• Wearables: heart rate, temperature, location inside the facility.
• Video in common areas: presence, behaviour patterns, incidents; facial
recognition is sometimes used.
2. Local processing
All data flows into an on-site server that:
• cleans and time-stamps the data;
• classifies events (e.g., “fall”, “restless movement”, “wandering”);
• generates alerts for staff;
• forwards information to the central AI engine.
Video is processed almost immediately to extract behavioural information, but recordings
are kept locally for about 48 hours.
3. AI analysis
The central AI system:
• produces risk scores for each resident;
• detects changes in behaviour or health patterns;
• determines what alerts appear on staff dashboards;
• feeds into staff performance scoring (e.g., response times, unattended alerts).
4. Storage
• Local storage:
◦ video: ~48 hours
◦ sensor/wearable raw data: ~30 days
◦ AI-generated alerts: ~6 months
• Central storage: aggregated metrics and analytics kept for 1–3 years.
5. Access
• Care staff: real-time alerts + limited history
• Nurses: full monitoring history
• Facility management: all data + performance dashboards
• Residents/families: only general summaries (consent considered inadequate)
• Number of care worker positions unfilled
According to the 2024 report of the National Institute for Social Research, the long-
term care sector in Vichtery had more than 48,000 positions vacant.
• Average duration of waiting lists for elderly persons seeking long-term care
• Surveys carried out between 2023 and 2025 showed that, in major cities, waiting
lists for subsidised residential care ranged from 24 to 38 months.

Question 2
Which non-residential institutions in Vichtery are authorised to provide medical care,
psychotherapy, psychological support, and related services to elderly individuals, and to
what extent are these individuals entitled to financial assistance to cover the associated
costs?
In Vichtery, elderly persons can access non-residential support through four main types of
institutions:
1. Community Geriatric Care Centres (CGCCs) – public outpatient clinics offering
basic medical care, physiotherapy, and chronic-disease management.
2. Mental-Health Outpatient Units (PMHUs) – hospital-based or licensed private
services providing psychotherapy and psychological support.
3. Day-Care and Social Support Centres – centres offering daytime supervision,
cognitive-stimulation activities, and limited nursing care.
4. Accredited Home-Care Services – agencies providing home visits by nurses,
physiotherapists, social workers, and, in some areas, psychologists.
Financial assistance exists but is fragmented:
• Standard public health insurance reimburses part of medical visits and
physiotherapy; psychotherapy is only lightly subsidised.
• A means-tested Elder Support Allowance can cover a large share of costs for low-
income individuals, but access became more restrictive after the 2025 Act.
• Some municipalities offer small top-up grants, creating geographic inequalities.

UCLouvain Saint-Louis Brussels
Question 1
The 2025 Law on Long-Term Care Transformation and Digital Monitoring provides, in point
iii, for the suspension until 2030 of the minimum staffing ratios previously imposed by the
Social Protection Code. In order to assess the regression in the level of protection granted
to clients — in particular when night teams consist of a single caregiver for 30 to 34
residents — could you please specify what those minimum ratios previously in force were?
Minimum Staffing Ratios Previously in Force (before the 2025 suspension)
Daytime minimum ratios (pre-2025 Social Protection Code)
1. General LTC units:
◦ 1 caregiver per 8 residents
◦ 1 nurse per 24 residents
2. Dementia-specific units:
◦ 1 caregiver per 5 residents
◦ 1 nurse per 18 residents
3. High-dependency units (mobility or medical complexity):
◦ 1 caregiver per 4 residents
◦ 1 nurse per 15 residents
Night-time minimum ratios (pre-2025 Social Protection Code)
1. General LTC units:
◦ 1 caregiver per 15 residents
◦ 1 nurse per 60 residents (on-call on site)
2. Dementia-specific units:
◦ 1 caregiver per 10 residents
◦ 1 nurse per 40 residents (on-call)
3. High-dependency units:
◦ 1 caregiver per 8 residents
◦ 1 nurse per 30 residents (on-call)

Question 2
The 2025 Long-Term Care Transformation and Digital Monitoring Act provides for an AI-
managed digital monitoring system in all long-term care facilities. We are wondering how
the data collected through the various technologies implemented (sensors, wearable
devices, continuous video monitoring in common areas) are processed by the facility.
Under the 2025 Act, each long-term care facility uses a unified AI-managed
monitoring system. Data processing works as follows:
1. Data collection
• Sensors: movement, bed-exit, door activity, fall-detection signals.
• Wearables: heart rate, temperature, location inside the facility.
• Video in common areas: presence, behaviour patterns, incidents; facial recognition
is sometimes used.
2. Local processing
All data flows into an on-site server that:
• cleans and time-stamps the data;
• classifies events (e.g., “fall”, “restless movement”, “wandering”);
• generates alerts for staff;
• forwards information to the central AI engine.
Video is processed almost immediately to extract behavioral information, but
recordings are kept locally for about 48 hours.
3. AI analysis
The central AI system:
• produces risk scores for each resident;
• detects changes in behaviour or health patterns;
• determines what alerts appear on staff dashboards;
• feeds into staff performance scoring (e.g., response times, unattended alerts).
4. Storage
• Local storage:
◦ video: ~48 hours
◦ sensor/wearable raw data: ~30 days
◦ AI-generated alerts: ~6 months
• Central storage: aggregated metrics and analytics kept for 1–3 years.
5. Access
• Care staff: real-time alerts + limited history
• Nurses: full monitoring history
• Facility management: all data + performance dashboards
• Residents/families: only general summaries (consent considered inadequate)

Free University of Tbilisi
Question 1
Is the team entitled to rely on other articles of the European Social Charter or relevant
provisions of EU law that may apply to the case and be interpreted in conjunction with the
fundamental articles mentioned in Part F of the case?
Yes.

Question 2
Do we assume that the European Federation for Dignified Ageing (EFDA) and the
International Alliance for Care Workers’ Rights (IACWR), the INGOs mentioned in Part F of
the case, are included on the CoE’s list of organizations entitled to lodge a collective
complaint?
Yes.

RESULTS

We are pleased to announce the RESULTS of the fifth edition of the Moot Court Competition on the European Social Charter, organised by the Academic Network on the European Social Charter and Social Rights (ANESC) with the support of the Council of Europe. The jury unanimously highlighted the high quality of the pleadings delivered on 24 April 2026 at UCLouvain Saint-Louis (Belgium).

At the conclusion of the hearings, the Free University of Tbilisi (Georgia) was awarded first place and also received the prize forBest Collective Complaint. The team members were Saba Sarjveladze, Valeri Sidamonidze, and Alexandre Mikeladze (coach: Nicolaos A. Papadopoulos). 

The runner-up was Vrije Universiteit Brussel (Belgium), represented by Marie Balasse, Roxanna Plateau, and Lien Versichele (coach: Guido Van Limberghen). This team also received the award for Best State Memorandum, while Roxanna Plateau was recognised as overall Best Pleader.

The jury extends its commendation to all seven participating teams for their dedication, enthusiasm, and high-quality work.

On behalf of the ANESC coordination team, we warmly thank this year’s jury, the participating students, and the outstanding host university team.

The 2026 Jury
 
 

The winning team (with George Theodosis, competition jury & ECSR member)
 
 

The runner up team (with George Theodosis, competition jury & ECSR member)