How TEEI Works: One Platform, Many Programmes

TEEI’s programmes run on shared building blocks. A new programme starts on parts that already work, and each part is there because research shows what it does.

TEEI runs its programmes on one platform. A learner practising English with a volunteer, a participant in a Skills Academy class, a mentee preparing for an interview and a company counting its employees’ hours all use the same building blocks: matching, live rooms, practice, feedback, booking and measured time. A new programme does not need a new system. It needs its own people, rules and goals, set up on blocks that already work.

The Educational Equality Institute (TEEI) is a nonprofit. Its free programmes connect people who are rebuilding their lives, today mainly people from Ukraine and women in Latin America and the Caribbean, with volunteers who offer connection, language practice, skills and guidance into work. Participants pay nothing, and neither do the companies whose employees volunteer.

This article explains how the model fits together in three layers: the building blocks the platform is made of, the research that shows why each block matters, and the programmes that run on them today. It ends with how a new programme starts, how we measure, and what we do not know yet.

Built through volunteer contributions. TEEI’s software platforms are built through volunteer work, with NOK 0 spent on paid software development. Infrastructure and third-party services are separate from that figure.

At a glance

• Four doors, no ladder. Connection, Language, Skills and Work have equal standing. A person starts where their need is, and no area is a requirement for another.
• One platform, shared blocks. Matching, live rooms, classes, practice, feedback, booking, a measurement register and Evenhall serve every programme.
• A mechanism behind every block. Each block exists because research shows what it does for learning, connection or work. No study has tested the model as a whole, and we say so.
• Hours from rooms, not calendars. On 15 October 2026 the public counter read 12,560 sessions and 9,462.9 hours of conversation across TEEI Language and TEEI Mentorship.
• AI proposes, people decide. Practice Companion, our AI practice space, opens to learners once it has passed L2SAFE, the benchmark TEEI Research built to test whether a model understands and answers a question as safely in learner language as in fluent language.
• Built to grow. New programmes start on the same blocks, and other NGOs will be able to run their own programmes on the platform, free, as a service.

Four doors, no ladder

TEEI’s offer has four areas of equal standing: Connection, Language, Skills and Work. Someone can come for a person to talk to, for language practice, for a skill or for guidance into work, and stay with that for as long as it helps. A stable buddy relationship is a good outcome without a course. A language class is useful without a career goal. A mentor can help an experienced professional move forward without sending them back to basic training.

The research explains why there is no ladder. In Denmark, rules that pushed refugees into work first brought quick, mostly precarious jobs, crowded out language learning and did little for women (Arendt, 2022). Sequences that demand language first and everything else later fail in the other direction: people wait, and the skills they arrived with lose value. The programmes that worked ran language alongside practical work rather than before it (Dahlberg et al., 2022). So the doors connect, but none is locked behind another.

The connections are offered, not imposed. A participant can bring a task from a Skills Academy class into a language conversation, a mentor can help choose what to learn next, and Practice Companion can give practice between classes. Many participants, especially women with caring responsibilities, have little time, so the design aims for one main activity and one supporting relationship at a time, with a clear next step.

The building blocks

Everything participants see is built from a small set of shared parts. Each programme uses the ones it needs.

Matching and relationships

People choose who they work with. A learner picks a conversation partner from the volunteers available in their language. A mentee browses approved mentors by field and experience. A woman in the Women Buddy Programme is shown her best matches on city, language, interests and availability, and picks one. The platform proposes; the person chooses, and a proposed match is never counted as a result. In TEEI Language, a volunteer can also mark themselves available now. A learner who sees them can start a conversation at once, the volunteer’s screen rings, and if nobody answers within two and a half minutes the session is cancelled and the learner told, even if every browser has closed.

The research says why choice matters. In the one randomised study that let women pick who they learned with, the choice was the active ingredient (Field et al., 2016). In the German buddy trial, fewer than one in five of those offered a buddy ever started, often because matching was slow or the language gap too wide (Jaschke et al., 2022). Fast matching, an easy start and the option to bring someone you know are design responses to that.

Live rooms with measured time

Live conversations, classes and workshops take place in TEEI’s own video rooms, built on Amazon Chime SDK and opened from the participant’s TEEI page rather than a link to another app. Before joining, a person checks camera and microphone in a lobby that shows the start time in both cities and what the learner asked for when booking. In the room, the other person fills the screen; captions, chat, raised hands and reactions stay at the edges. Audio and video travel directly between browsers and the media service, never through TEEI’s servers. For every joining, leaving and dropped connection, the room emits an event. Those events, settled after the session, are what become a verified hour.

The rooms are tuned for weak networks. When we measured 934 production sessions on 7 September 2026, packet loss was the problem, not latency: in one session in ten it reached about 21 percent or more. So the rooms send redundant audio, allow five minutes to reconnect, and keep the last video frame rather than showing a black box during a reconnect. The engineering note on Amazon Chime SDK gives every setting and why.

Classes

Teacher-led and volunteer-led classes use the same rooms with roles. In a webinar, a listener receives audio and video without sending it, and a host can invite someone to speak and return them to listening. Workshops, clinics and classes are built for conversation, so everyone in them can speak. Instructors who apply on their own are approved before they teach, while a colleague a company sends creates and teaches classes and workshops on topics they choose. Participants enrol, and a class certificate follows confirmed attendance and reviewed work. TEEI Language Courses add materials and assignments in itslearning. In TEEI Admin, AI support for classes proposes which class a participant fits and helps follow them up, and a teacher or coordinator decides.

Practice

Practice Companion is the engine for structured practice between conversations with people. One AWS Lambda function, behind a signed token, calls Claude models on Amazon Bedrock through EU cross-region inference, and an Amazon Bedrock guardrail judges what reaches the learner. Every model call returns a structured answer through a forced tool, a fixed answer format the model has to fill in, so a correction carries its category from a versioned list, and a correction has to point at the learner’s exact words before it is stored as evidence. The same engine, at its own route, gives AI first feedback on Skills Academy work, and a career companion on its own function helps mentees prepare between mentor sessions, in at most 250 words, referring the mentee back to their mentor and never claiming a relationship of its own.

Feedback and assessment

Every score the platform’s AI gives must quote the person’s own work, and a score whose quote cannot be found in the work is refused. The quote is checked twice, once in the function and again in the backend. A reading carries its provenance: the model, the prompt, the schema and the rubric, and for a level reading a fingerprint of the scorer, so each stored score names the judgement that produced it, and a changed scorer can never look like a changed person. In Skills Academy, AI gives the first feedback and a person on the class team reads it before the scores count; in TEEI Language, a level reading calibrates the companion and is never a grade.

Booking and follow-up

Availability, booking, notifications, reminders and rebooking are built once and shared across the programmes, and each programme sets its own rules for them. A volunteer sets the hours they can give; a participant books the sessions, or in a course is placed in a class. Nobody has to recruit an audience, and nobody has to learn a second system when they move from one programme to another.

The measurement register

Every outcome has a written definition before it has a value. Activity is counted from room events. Results are measured with named instruments on named dates. Effects need a comparison, and the register says which. A new measure starts as “Reserved”: the rule is written and the first record is still to come. That is a status, not a zero. The same definitions apply across programmes, and will apply across other organisations running on the platform, so that work can be compared and still reported separately.

Company reporting: Evenhall

Evenhall is the free workspace for companies. Managers invite employees into TEEI Language, Skills Academy and TEEI Mentorship, where they take part in the same rooms as everyone else. Evenhall reads verified participation from the programme platforms overnight. For one-to-one sessions, an employee’s hours are the time both people were connected, from sessions settled as completed; for an instructor, the measured time in the room. Where nothing was measured, nobody gets hours. A manager builds the report from those records, with every figure linked to its source, reviews it and shares it. The company sees counts and hours, never participants’ names, and never what was said.

Two rules across all eight

AI checks, drafts and proposes, and people make the decisions that matter for a person. Five AI checks run for participants today: screening of volunteer applications, safeguarding in chat, a help assistant that answers only from TEEI’s help articles, review of return plans, and a guide for group practice. In every case either the platform’s written policy or a member of staff makes the final decision, and the checks record outcomes and counts, not the text they read.

Data stays with its purpose. A learner’s practice history does not reach their volunteer. Health information never reaches a career profile. Company reports never contain participants’ names. And every person who joins a room is handed to the video service under a pseudonym that changes with every meeting.

The programmes at the top bring their own people, roles and rules. Everything under them is shared, which is what lets a new programme start on parts that already work.

The research behind the blocks

TEEI did not start from technology. It started from what research says makes adult learning, connection and the move into work succeed, and built a block for each mechanism. One rule runs through everything below: a research finding justifies a mechanism, a design choice is described as TEEI’s choice, and a result from TEEI needs TEEI’s own data.

Interaction and feedback

Conversation is not a reward at the end of language learning. It is one of its engines. Across 28 studies, conversational interaction had a medium effect on immediate tests and a large effect on delayed tests (Mackey & Goo, 2007). Corrective feedback adds to it, with a medium effect across 33 studies that held over time (Li, 2010). Volunteers can deliver this kind of support online: fully online one-to-one tutoring by trained volunteers raised learning by about 0.2 standard deviations, and more with a higher dose (Carlana & La Ferrara, 2025).

This is why every TEEI Language learner practises with people, not only with software, and why a correction in Practice Companion is anchored in the learner’s own sentence.

Spaced practice, and AI with a teacher’s discipline

Practice spread over days and weeks beats the same practice crammed together, across 48 experiments with 3,411 learners (Kim & Webb, 2022). AI makes spaced practice possible at any hour, but the evidence also sets limits. In a field experiment with about a thousand secondary school students, open access to GPT-4 raised scores while students had it and left them 17% worse off once it was taken away. A tutor version that gave hints instead of answers largely removed the harm (Bastani et al., 2025). And when chatbots are compared with other learning technology rather than with nothing, their advantage shrinks to a small one (Lyu et al., 2025).

Practice Companion is built on those findings. It asks the learner to produce language, gives one thing back at a time, and checks later whether a form has become theirs without help.

Belonging and small groups

Relationships can turn learning into action. In a randomised study of 636 women in India, business training changed women’s business activity only when they were invited to attend with a friend, and the effect was strongest where social norms limited women’s freedom of movement (Field et al., 2016). Belonging matters too, but only where there is something real to belong to. In a trial with 26,911 students at 22 institutions, a short exercise on belonging raised first-year completion by one to two percentage points, and only in settings that offered real chances to belong (Walton et al., 2023). Small groups with a shared task do more than open channels: women placed in all-female virtual groups on a career platform were about seven percentage points more likely to finish their training on time (Melin, Yang & Goulas, 2026).

Connection is also a goal in its own right. Four weeks of empathy-focused phone calls from trained laypeople reduced loneliness on the UCLA loneliness scale, though not on a second measure (Kahlon et al., 2021). A review of 280 studies found that loneliness interventions help in the short term, with low certainty and wide differences between types of intervention (Lasgaard et al., 2026). A German trial of volunteer buddies for refugees found more contact with locals and higher satisfaction with housing, but no effect on employment (Jaschke et al., 2022).

TEEI therefore treats connection as an outcome, not as a funnel into employment, and builds a separate bridge to work.

Mentoring and weak ties

Professional mentoring helps when it is aimed at work. In a randomised evaluation in Poland, women moving into technology who were mentored by senior women in the field were 15 percentage points more likely to be working in tech a year later (Athey & Palikot, 2026). Experiments with LinkedIn’s connection recommendations, covering 20 million users, found that moderately weak ties produce the most job mobility, especially in digital industries (Rajkumar et al., 2022).

TEEI Mentorship therefore lets mentees choose mentors by field and experience, and keeps mentoring separate from buddy relationships.

Proof of skill

People are hired on what employers can see. In Ethiopia, a workshop that certified young job seekers’ skills raised their earnings by 25% four years later, while a transport subsidy that only made searching cheaper had no lasting effect (Abebe et al., 2021). In Poland, an online portfolio programme costing about 15 US dollars per participant raised the share working in tech by 11 percentage points (Athey & Palikot, 2026). Learning research points the same way: practice activities are associated with about six times the learning gains of reading or watching (Koedinger et al., 2015), and feedback that explains the task and the process outperforms praise alone (Wisniewski, Zierer & Hattie, 2020).

Skills Academy therefore ends in work a person has done and someone has reviewed, not only in a certificate.

Why the connections matter

The strongest evidence is for programmes that combine parts. In Gothenburg, 140 low-educated refugees were randomised to a programme of three months of intensive Swedish, six months of supervised work practice with continued language classes, and job-search support from caseworkers. About 30% of the treatment group were employed each month in the year after the programme, against about 15% of controls, at a cost of around SEK 27,000 per participant. Better language skills explained only a small share of that effect; most of it came through practice, supervisors and matching (Dahlberg et al., 2022). I-BEST in Washington State teaches basic skills and a trade together, with a dedicated adviser. In a randomised evaluation it raised college credits and short-term credentials for adults with limited basic skills, though effects on employment and earnings after six years were mostly not detectable (Martinson & Glosser, 2022). Year Up, which combines training, support services and employer connections, raised earnings by 30% in the seventh year after random assignment (Fein & Dastrup, 2022).

Those studies test whole packages. They cannot say which parts were necessary. A few studies isolate one connection, and they point the same way: a social component made training effective for women (Field et al., 2016), intensive human case management raised completion where emergency financial aid alone did not (Evans et al., 2020), and signalling skills to employers raised earnings where cheaper searching did not (Abebe et al., 2021). Across 207 studies of active labour-market programmes, average effects are close to zero in the short run and turn positive two to three years after a programme ends, most for programmes that build skills (Card, Kluve & Weber, 2018). A model like TEEI’s should not be judged at three months.

Three connections have the strongest support, and they are the ones TEEI builds first.

The evidence, connection by connection

No study has tested a model like TEEI’s: four areas of equal standing, delivered digitally, across borders and largely by volunteers. The case for it rests on evidence about each mechanism. It is a reasoned design, and we treat it as one.

The programmes running today

Each programme takes the blocks it needs and adds what is its own: who it is for, the roles, the words, the rules and the safeguarding.

TEEI Language: conversation with people

TEEI Language is conversation with people. A learner books a volunteer conversation partner for 30, 45 or 60 minutes and talks, one to one, on video. There is no lesson plan, no grading, no teaching qualification and no native-speaker requirement. Volunteers need to be comfortable in the language they offer, and TEEI offers twelve, including English, Spanish, Ukrainian, Polish and Norwegian. Learners choose the volunteer and the time. Volunteers who like groups host Speaking Clubs, where a conversation card gives the group something to talk about and the host can invite someone to speak or settle the room.

A learner’s week. Picture Olena, 38, a Ukrainian accountant in Poland, who wants to work in her field again. She books a Polish-speaking volunteer twice a week and asks, at booking, to practise talking about her work. In the lobby she sees the start time in her city and the volunteer’s and checks her camera. In the session, she asks for live captions, which start once the volunteer agrees, so she can see how a word she just heard is spelt. After the call, she saves the transcript to her own account. TEEI keeps no copy otherwise.

Why it is built this way. Conversational interaction had a medium effect on immediate tests and a large effect on delayed tests across 28 studies, which suggests it sets off learning that continues after the session (Mackey & Goo, 2007). Fully online one-to-one support from trained volunteers works, with a higher dose working better (Carlana & La Ferrara, 2025). A fixed partner builds trust and negotiates meaning more freely (Plough & Gass, 1993), while new partners force more explicit communication, so learners can keep a regular partner and still meet new people. Language anxiety is consistently linked to lower performance (r = -0.36; Teimouri et al., 2019), which is why the session has no grades and no assessment.

Built for everyday life in Ukraine. For many participants, a power cut or a phone switching networks mid-sentence is part of an ordinary day. The rooms use an audio profile with redundancy, so a lesson survives losing the picture but not the voice, and they allow five minutes to reconnect instead of the default two, so a conversation interrupted by a power cut continues when the person returns. Background noise is reduced in the browser, and background blur lets someone take part without showing their home.

What is measured. Time both people were connected, settled after the session. Not attention, and not learning. Those need the measures below.

TEEI Language Courses: structured classes

TEEI Language Courses are teacher-led online group classes, two to three sessions a week, with materials and assignments in itslearning. They run today. The teacher owns what the research says a teacher should own: progression, diagnosis, explicit explanation of form and assessment. Explicit instruction has a clear advantage for both simple and complex structures (Norris & Ortega, 2000; Spada & Tomita, 2010), and oral corrective feedback works in classrooms, not only in laboratories (Lyster & Saito, 2010).

The classes and the conversations work on the same goals without sharing the learner’s private history. A learner can bring a topic from class into a conversation with a volunteer, and Practice Companion practises what came up in class.

In TEEI Admin, AI support for the classes proposes which class a participant fits, based on level, goal, language and availability, and helps follow participants up along the way. A teacher or coordinator confirms the placement, and a participant can ask to change class without giving a reason.

Practice Companion: structured practice between conversations

Practice Companion is the part of TEEI Language a learner uses on the days between conversations with people. It has four rooms around one desk. In Write, the learner writes a sentence about their own life and gets one thing back. In Listen, they hear short everyday dialogues, written and reviewed by people, recorded once with synthetic voices, with the words hidden until wanted, and answer three questions that are never scored. In Say it, they talk a situation through by voice: a receptionist with two appointment times, a job interview, a new colleague on the first day. In the level conversation, they answer six written tasks and are read, not taught.

The rooms share one memory: the learner’s level, the situation they are practising for, and a shelf of lines they have chosen to keep. A kept line can travel into the next booked session, and that line is the only thing from the companion the volunteer sees. What the companion knows about a learner stays with the learner.

One correction, four chances. After a correction, the companion looks for the corrected form in the learner’s own later sentences in four windows: the same session, and around a day, a week and a month later. When a window is due, the conversation steers towards a moment where the form comes up naturally, at most once a session and never as a drill. This follows the evidence that spaced practice beats massed practice across 48 experiments (Kim & Webb, 2022), and the evidence that AI without pedagogical structure can produce better answers now and weaker learning later (Bastani et al., 2025). A better sentence written with help is not the measure. The same form, a week later, in the learner’s own words, is.

Reading a level without inflating it. The level conversation is six written tasks from a versioned plan. Code chooses the tasks; the model only words each question. A scorer reads the whole conversation three times, must quote the learner’s own words for every criterion, and keeps the lowest band when the readings disagree. Progress between two conversations is a word, up, same or down, and only when both used the same instrument and different questions, so a new model can never look like progress. The level sets how the companion writes to the learner. It is never a requirement for a room, never a right to anything and never a certificate.

Not open yet. Practice Companion opens to learners once it has passed L2SAFE, the benchmark TEEI Research built to test whether a model understands and answers a question as safely in learner language as in fluent language. We are also tuning prompts and model settings so that learners get the best possible experience, and finishing the production setup of the voice path. The engineering note Inside Practice Companion describes every room, the certification results and the architecture.

Skills Academy: skills shown in work, not in certificates

Skills Academy is where volunteers with professional skills teach live: one-off workshops, clinics, expert Q&A sessions and webinars, and classes that meet weekly for two to six sessions. Teaching is open to any employee of a partner company, in any field: HR, finance, law, marketing, leadership, product, data, software. A company can teach the skills behind its own tools, as long as the session page says whose technology it is, it teaches the skill rather than the sale, and participants’ details never go to the company. Alongside the live sessions, participants study at their own pace on DataCamp and take Google Career Certificates on Coursera, where those platforms issue their own certificates.

A participant’s class. Picture Mariela, 29, who sells in informal trade in Lima and wants to reach customers online. She has a smartphone but little experience with other digital tools, so she starts with a short class on spreadsheets and basic data, planned within four hours a week in the evenings. Each week ends in a task on real, anonymised data rather than in a video. She scores her own work against the rubric’s three criteria before a reviewer sees it. In a clinic, an instructor looks at her spreadsheet and shows her where an AI-suggested formula went wrong and why. Five weeks in, she solves a new dataset without AI in 45 minutes. The class certificate follows when the teaching team confirms her attendance and reviewed work. Her work sample, not the certificate, is what she shows an employer.

Why it is built this way. Open online courses show what content alone does: 52% of those who registered for MIT and Harvard courses on edX never entered the material, and completion barely moved in six years (Reich & Ruipérez-Valiente, 2019). What turns content into skill is around it. Doing practice activities is associated with about six times the learning gains of reading or watching (Koedinger et al., 2015). Feedback has a medium effect overall (d = 0.48); feedback that explains the task and the process does far more (d = 0.99), while praise and punishment alone do the least (d = 0.24) (Wisniewski, Zierer & Hattie, 2020). Having peers assess work improves performance (g = 0.31), also online (Double, McGrane & Hopfenbeck, 2020). Support should fall away as competence grows, since help that aids beginners can hinder the more experienced (Kalyuga et al., 2003).

AI as a judgement skill. AI and data is a field of its own in Skills Academy, from AI foundations to responsible AI, and AI is part of how participants learn in every other field. The evidence sets the rules. In a field experiment with about a thousand students, open access to GPT-4 raised practice scores by 48% and left students 17% worse off on the test once it was removed; a tutor version that gave hints instead of answers largely removed the harm (Bastani et al., 2025). Among 52 experienced developers learning a new library, those who used an AI assistant scored about 17% lower on a comprehension quiz than those who coded by hand (Shen & Tamkin, 2026). So when using AI is the skill being learned, the AI tool is allowed in the assessment. When the assessment is of understanding without help, the companion does not lead the participant through it, and every pathway has at least one checkpoint without AI.

Feedback that quotes you. AI first feedback on submitted work is built and not yet open. Every score quotes the participant’s work, and the quote must appear in it exactly once; a single quote that fails refuses the whole reading. The participant scores their own work first, and a reviewer does not see those scores until they have saved their own. There is no total, no grade and no pass or fail. A person on the class team reads the AI’s feedback before the scores count. Work a person has reviewed is labelled verified; work only AI has scored is labelled AI-assessed.

What is measured. The quality of practical work against criteria, independent solution of a new task without AI, and a repeat after four to eight weeks. Enrolment, logins and videos watched are operating data, not learning.

TEEI Mentorship: guidance into work

TEEI Mentorship is for people, especially Ukrainians, who are rebuilding a career or finding their way in a new job market. Mentees browse approved mentors by field, experience, language and availability, and request or book a session. Mentors give career and professional guidance: a CV, interview preparation, a job-search strategy, a change of career, how hiring works in their industry. They do not give therapy or legal advice, and they do not promise anyone a job.

Why it is built this way. Mentoring is not one thing. The research separates role model, adviser, coach, sponsor, network introduction and practical job-search help, and finds that the last three drive employment effects. In a randomised evaluation in Poland, women moving into technology who were mentored for four months by senior women in the field, with a consistent emphasis on introductions to hiring managers, were 15 percentage points more likely to be working in tech a year later, from 46% in the control group (Athey & Palikot, 2026). General social mentoring for refugees in Germany, by contrast, had no measurable effect on employment (Jaschke et al., 2022). The LinkedIn experiments cited above point the same way for mentoring: moderately weak ties, the kind a mentor’s introduction creates, produce the most job mobility, especially in digital industries (Rajkumar et al., 2022). Help with job search mattered most for those who lacked networks (Battisti et al., 2019).

Three things follow. Mentoring is professional and aimed at work, and it is kept separate from buddy relationships. Mentees choose, and get structured information about which mentors have access to their target industry, so the choice does not follow sympathy alone. And the outcomes are a ladder rather than one rung.

The outcome ladder. Job-search activity, interviews, offers, work, hours, income, stability over three months, whether the job uses the person’s qualifications, and progression over time. Each step is recorded separately, at three, six, twelve and twenty-four months. A move into work below a person’s level is not the same as a good match. For Ukrainians in Europe, where 57% of those aged 20 to 64 were in work in 2025 and about 60% of them below their skill level (UNHCR, 2026), qualification match is the measure that matters most.

Connection: TEEI Women and Together for Ukraine

TEEI Women and Together for Ukraine are the frames through which TEEI reaches the communities it began with. The Women Buddy Programme matches women with a buddy for connection and support, with a welcome relationship first, then support, then a pathway buddy for women who want to move into language, skills or work. A buddy is never responsible for someone’s learning progress, job search or medical questions. The relationship is the point.

Why it is built this way. The strongest causal evidence for women is that learning together with someone they know changes what they do afterwards. In a randomised study of 636 women in Ahmedabad, business training changed women’s business activity only when they were invited to attend with a friend, and the effect was strongest where social norms limited women’s freedom of movement (Field et al., 2016). Belonging works only where there is something real to belong to (Walton et al., 2023). Small, structured groups do more than open channels: women in all-female virtual groups on a career platform were about seven percentage points more likely to finish training on time (Melin, Yang & Goulas, 2026). Light digital nudges to find a study partner, by contrast, did nothing measurable across 45,000 students in Ecuador (Asanov et al., 2023).

So the programme lets women bring a friend, invests in fast matching, trains buddies in respect and reciprocity rather than in content, plans the end of a relationship so that loneliness does not return when a time-limited match ends, and removes practical barriers: times that fit school and childcare, short sessions, and the option to take part with children present. For Ukrainian refugee mothers in Germany, whose children attend childcare, participation in language and integration courses is about 25 percentage points higher (Gambaro et al., 2026). No buddy can replace childcare.

What is measured. Time from sign-up to first meeting, the share with at least three meetings, belonging and loneliness measured separately, and whether the relationship continues. Connection is not reduced to a funnel into employment.

One Connection: peer support from someone who has been there

One Connection, now in development, offers one-to-one peer support to people going through a stem cell or bone marrow transplant, from someone who has been through it. Most transplant patients today get no formal peer support, and most existing programmes still match by hand over the phone. Matching in One Connection uses experience, phase of treatment, language and the kind of contact a person wants, not a general interest profile with a diagnosis added. Peers share experience, never treatment advice, have their own introduction, and have a clear path to support from TEEI.

A pilot randomised trial of five telephone sessions with a transplant survivor found the approach feasible and acceptable, with preliminary psychosocial benefits (Amonoo et al., 2026). It is a pilot. It is not evidence for One Connection, and loneliness interventions in general help in the short term with low certainty (Lasgaard et al., 2026). One Connection will measure what peer support does for the people in it: perceived understanding, relevant support, depression, anxiety, quality of life after transplant and loneliness, with validated instruments.

Health information from One Connection never reaches a career profile, a company report or ordinary buddy matching. The programme reuses the platform’s components, not its data.

TEEI Meetings

Private meetings with invited guests, who join from a personal link without an account. Hosts, speakers and listeners each get exactly what their role needs.

Evenhall

The free workspace for companies. Managers invite employees into TEEI Language, Skills Academy and TEEI Mentorship, where they take part in the same rooms as everyone else. Evenhall reads verified participation overnight and turns it into reports, with every figure linked to the record behind it.

How a new programme starts

Two programmes show how this works in practice.

Skills Academy did not get a platform of its own. It runs on the TEEI Mentorship platform under its own address, uses the same rooms, with host, speaker and listener roles for webinars, and uses the same engine as Practice Companion for AI first feedback on submitted work. What was new was the programme itself: approving instructors, enrolment, the class formats and the certificate rules.

One Connection is built on the components of the Women Buddy Programme. What it adds is what makes peer support for transplant patients its own: matching on experience and phase of treatment, a defined peer role, an introduction for peers and a path to support. It reuses parts, not data. Nothing from One Connection reaches a career profile, a company report or ordinary buddy matching.

Every new programme follows the same six decisions.

1. Who it is for, and what it promises them.
2. Roles and matching: who the participants meet, and how they are matched.
3. Formats: one-to-one, groups, classes, practice, or a mix.
4. Rules and safeguarding: what is shared, what never is, and who steps in when something goes wrong.
5. Outcomes, defined in the measurement register before any data exists.
6. Set-up on the shared blocks. Where AI works directly with participants, it opens only after its evaluation has passed, as Practice Companion does with L2SAFE.

Everything else, from rooms and bookings to verified hours and company reports, is already there.

AI at TEEI: where it checks, and who decides

TEEI uses AI in five places where it meets participants, and in each one a written policy or a person makes the decision.

Screening volunteer applications. When someone applies to volunteer, a model reads the application and returns a structured verdict: a decision, red flags, ambiguities, positive signals and a confidence score. For TEEI Language conversation partners, the platform may approve an application automatically, and only on a clean pass against a written policy. Mentors who apply on their own and course teachers go to a person, whatever the verdict. The application text itself is not stored.

Safeguarding in chat. Chats between participants are theirs. No one at TEEI reads along. The platform’s own software checks each message and rates whether it may need attention. It keeps the rating, not the text. Only a message rated as a possible concern reaches a person at TEEI responsible for safeguarding, who decides what happens.

A help assistant that answers only from TEEI’s own articles. It checks its citations against those articles before an answer is shown; when it cannot answer, the person is told so and can reach a member of staff. A question that touches on safety gets emergency guidance and no model text at all.

Review of return plans. When a volunteer has been unlisted after a breach and submits a plan to return, a model reports what the plan contains. It does not decide. The platform’s policy decides, and at the strictest level a member of staff does. The model never relists anyone.

A guide for group practice. The volunteer leading a Speaking Club can ask for a guide pitched across the learners’ levels. The volunteer decides whether to use it.

A prompt that decides something cannot ship without a passing evaluation. Each such check has a recorded test set, from 14 to 94 cases as of 14 October 2026, and a threshold it must meet: zero false automatic approvals for screening, zero escapes for safeguarding. If the prompt, the decision rule or the output format changes, the fingerprint no longer matches the committed receipt, and the deployment stops until the evaluation has been run again and passed. All product AI runs on Amazon Bedrock through EU cross-region inference, and TEEI leaves model logging off, so its own AWS account keeps no copy of prompts or replies.

Practice Companion is the sixth place, and the one that works with learners directly over time. That is why it opens only after L2SAFE.

What we chose not to build

Some of the model is defined by what is left out.

• No persona. The companion has no name, no face and no avatar. It is not a friend, and it does not pretend to be one.
• No score to chase. No counters, streaks, points or mastery percentages. Progress is a word, never a number, because CEFR bands, the A1 to C2 levels used to describe language ability across Europe, are not a scale you can do arithmetic on.
• No level as a gate. A level sets how the companion writes to a learner. It never decides access to a room, a class or a person.
• No blended reading. A stored level is one whole reading with its own quotes, never an average of three.
• No model choosing the questions. The task plan is code; the model only words a question.
• No free text to the volunteer. The shelf carries one line the learner chose. The volunteer sees that and nothing else.
• No audio stored. Spoken practice is stored as text.
• No media through TEEI’s servers. Audio and video go between browsers and the media service, never through TEEI.
• No hours from calendars. A booking says two people planned to meet. Hours come from the room.
• No outcomes claimed from activity. A session record proves that time was given. It does not prove what the time achieved.
• No fees, and no donation as a hidden next step. Participation is free for participants and for the companies whose employees volunteer.

What comes next

More programmes on the same platform, for more people. TEEI’s offer is widening beyond the groups it began with, and a new group does not need a new system: it needs the six decisions above.

The next step reaches beyond TEEI’s own programmes. Other NGOs will be able to run their own programmes on the platform, free, as a service. They bring the participants, the volunteers and the expertise, and keep responsibility for their programme. The platform brings booking, rooms, practice, feedback and verified records, with the same definitions of activity TEEI uses, so that work by different organisations can be compared and still reported separately.

The aim is reach through many operators on shared infrastructure, rather than one organisation coordinating every relationship itself.

How we measure

We keep three things apart: activity, results and effects.

Activity is who took part, in what, and for how long. On the public counter and in company reports, a verified hour is time that both people were connected to the same room, settled after the session, with at least five minutes together; a person’s own record counts all the time they were measured in the room. A session with missing events is marked unknown, never a no-show, because a missing signal is not proof that someone stayed away. By 14 October 2026, 1,440 one-to-one sessions on the current platform had been settled as completed this way. The public counter, which combines completed sessions from the platform TEEI used before with those on the current one, read 12,560 sessions and 9,462.9 hours of conversation across TEEI Language and TEEI Mentorship on 15 October 2026 at 19:02 UTC, with members in 148 countries.

Results are what changed for the person: a skill shown in new work, a level read from a conversation, a step towards work. Loneliness, belonging and the value of peer support are getting their own definitions in the register. Loneliness will be measured with the three-item UCLA loneliness scale (Hughes et al., 2004) at the start and after about twelve weeks; the timing is TEEI’s design, not part of the scale. Until data exists, these measures show “Reserved”. That is not zero.

Effects are what a programme caused, and they need a comparison. The first one we propose asks whether proactive connections, an offer of a buddy in the first week and a planned mentor conversation after the first practical task, add value over open access to the same offers.

Where AI measures, it is tested before it counts. Practice Companion’s level scorer reads every conversation three times and keeps the lowest band when the readings disagree. On 15 test conversations frozen before their first run, as of 14 October 2026, it matched the intended band every time, a result consistent with a true match rate as low as about 78 percent. Every reading stays labelled uncalibrated until blind ratings by experienced people show how far they agree.

What we do not know yet

• The model as a whole. No study has tested it. The case rests on evidence about each part.
• Language and work outside the Nordic setting. The strongest evidence linking language to work comes from refugees learning the host country’s language in the Nordic countries. For women in Latin America learning English for remote work, the evidence is thinner and mostly correlational.
• A digital, cross-border buddy. The friend effect was found in face-to-face training in India. A buddy met online, across borders, has not been tested.
• AI with adults rebuilding their lives. Most studies of AI in learning are short and involve students.
• Time. Many participants have very little of it. A model with many parts can become a burden if it asks for too much at once.
• TEEI’s own outcomes for this model. The register already holds first outcome rows for TEEI Language, such as recorded level changes and learners’ own confidence ratings. What does not exist yet is the comparison that would show what the model as a whole causes. We will publish outcomes as they arrive, with their definitions, sample sizes and missing data.

Who the model serves, and why that shapes it

The people TEEI works with today face different barriers, and the research shows that the same offer does different work for each of them.

Ukrainians in Europe are often highly qualified. Among those aged 20 to 64, 57% were in work in 2025, 22 percentage points below host-country citizens, and about 60% of those in work were below their skill level (UNHCR, 2026). Some knowledge of the local language raised the share in work by 13 percentage points, yet higher language levels brought no further gain, and most of those in work were in jobs below their qualifications. For this group the barrier is not a lack of competence but the move from competence to suitable work: recognition of qualifications, professional language and networks. The connection that matters most is Language with Work, in parallel.

Women in Latin America and the Caribbean face a different market. Women’s labour-force participation is 52.1% against 74.3% for men, and nearly half of all workers are informal (ILO Labour Overview 2024). Internet use is close to gender parity, but practical digital skills are not: in one six-country study, 35% of women against 24% of men said they could not use a smartphone (Agüero et al., 2020). Women also carry up to twice as much unpaid work as men (ILO, 2026). For this group, Work can mean formalisation, higher income from their own trade or remote work rather than a first job, Skills must start lower than an AI course, and time is the scarcest resource.

Loneliness cuts across both. About one in six people worldwide report it, with small differences between men and women (WHO Commission on Social Connection, 2025). Social isolation, the objective state of having few relationships, is a different thing: a woman can have a close family network and still be lonely, or lack professional ties. TEEI keeps the two apart in its measurement register.

The model is built so that a new group with a new mix of barriers needs a new configuration, not a new platform. That is also why TEEI’s offer is widening beyond the groups it began with.

The five articles this one builds on

• How TEEI Turns Amazon Chime SDK Events into Verified Hours
• Inside Practice Companion: Rooms, Evidence and Level
• How TEEI Runs Its Platforms on Cloudflare Workers
• What TEEI Runs on AWS
• How Corporate Volunteering Works at TEEI

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