AI Protocols for Research and Higher Education: Interviews, Research Meetings, and Project Groups

Why Research and Higher Education Need a Different Kind of AI Protocoling

Research and higher education do not simply hold “meetings.”

They conduct research interviews, qualitative interviews, focus groups, project group sessions, research meetings, supervision talks, doctoral colloquia, lab meetings, ethics discussions, study coordinations, third-party funding meetings, practice partner workshops, evaluations, university council meetings, faculty meetings, institute meetings, teaching development workshops, and internal clarifications.

Information continuously arises that may become important later:

1. Interview statements
2. Research questions
3. Hypotheses
4. Methodological decisions
5. Project pending items
6. Tasks
7. Responsibilities
8. Deadlines
9. Ethics questions
10. Consents
11. Data protection topics
12. Review comments
13. Literature references
14. Data management topics
15. Publication ideas
16. Project decisions
17. Next steps

In research and higher education, documentation is not just administration. It is part of scientific quality, traceability, data management, ethics, project management, and knowledge transfer.

This is exactly where Meeting Metrics comes in.

Meeting Metrics is a Swiss AI solution for universities, universities of applied sciences, research groups, and scientific teams that automatically creates structured transcripts, summaries, tasks, pending items, research notes, and reports from interviews, research meetings, project groups, and internal sessions – with Swiss German support, Swiss data hosting, optional on-premise installation, and personal support for implementation, templates, and data protection issues.

Summary: Why Meeting Metrics Is Relevant for Research and Higher Education

Research teams have many conversations but little time for follow-up work.

Professors, doctoral candidates, postdocs, scientific staff, project leaders, study coordinators, research assistants, and university administrations spend a lot of time transcribing interviews, summarizing research meetings, tracking project tasks, documenting supervision talks, preparing workshop results, and retrieving information later.

Meeting Metrics helps exactly there.

During the conversation, the focus can be more on content, method, person, research question, or project decision. After the conversation, transcripts, summaries, tasks, research notes, pending items, and reports are automatically prepared. Through templates and context files, universities and research groups can adapt the results to their own research designs, interview guides, ethics requirements, project structures, and university processes.

The main benefit is not having “a transcript.” The benefit is making research and project work more quickly usable from conversations.

Meeting Metrics does not replace scientific analysis, coding, interpretation, ethics review, or academic responsibility. The solution supports preparation, structuring, and follow-up. Responsibility for research, methodology, publication, data protection, consent, and scientific quality naturally remains with the responsible persons.

Why AI Is Currently Relevant in Swiss Research and Higher Education

AI is no longer just a niche topic at Swiss universities and in research.

Swissinfo reported in 2025 that Swiss universities want to make a large multilingual AI model publicly available. It was developed on public infrastructure, focusing on multilingualism and openness. This shows: Switzerland positions itself in the AI field not only as a user but also as a research, infrastructure, and trust location.

In September 2025, Apertus, an open, multilingual Swiss language model, was released by ETH Zurich, EPFL, and the Swiss National Supercomputing Centre. Inside IT reported that there are two model sizes and that the models were released under an open-source license, enabling applications in education, research, society, and economy.

For universities and research, this is an important signal: AI is not only used as a tool but also understood as part of research infrastructure. At the same time, daily research practice requires solutions that are not just generic chatbots but support concrete processes: interviews, transcripts, research notes, project meetings, pending items, data protection, consents, and structured follow-up.

Meeting Metrics addresses exactly this practical level.

Research Needs More Than Transcription

Many tools promise transcription. For research, that is often not enough.

A transcript is only the beginning. Then the real work begins:

1. What is relevant for the research question?
2. Which topics repeat?
3. Which statements need to be verified?
4. Which quotes are potentially usable?
5. Which follow-up questions arise?
6. Which methodological notes are important?
7. Which tasks arise for the research team?
8. Which data must be anonymized?
9. Which points belong in data management?
10. Which insights must be shared with practice partners?
11. Which decisions were made in the project team?

Meeting Metrics supports not only transcription but also structuring.

Depending on the template, the following can arise from a conversation:

1. Transcript
2. Interview summary
3. Topic overview
4. Research note
5. Method memo
6. Task list
7. Pending items list
8. Project update
9. Workshop protocol
10. Decision overview
11. Management summary
12. Follow-up email
13. Report draft

Scientific analysis remains with the research team. Meeting Metrics provides the basis, not the interpretation.

Interviews Remain Central – But Follow-up Is Laborious

In many research projects, interviews are a central component.

This applies especially to:

1. Qualitative research
2. Social sciences
3. Educational research
4. Health research
5. Nursing research
6. Psychology
7. Market and usage research
8. Public policy
9. Administrative research
10. Design research
11. Ethnographic research
12. Evaluations
13. Practice projects at universities of applied sciences
14. Master's and doctoral theses

Interviews provide rich data. At the same time, they are laborious to follow up.

Often, researchers must:

1. Secure audio recordings
2. Observe consents
3. Create transcripts
4. Anonymize statements
5. Identify core topics
6. Verify quotes
7. Prepare codings
8. Record follow-up questions
9. Write methodological memos
10. Derive project tasks
11. Store data correctly

Meeting Metrics can support here by generating structured transcripts, summaries, topic overviews, tasks, and research notes from interviews.

Important: The solution does not replace qualitative content analysis, grounded theory evaluation, thematic analysis, or scientific interpretation. It reduces the raw work and improves the starting point for clean research.

Digitalization Changes Research and Teaching

Digital transformation changes not only the economy and administration but also science, education, and university organization.

SRF reported in 2026 about the National Research Program NFP 77 on digital transformation. The focus is not only on technology itself but on how digitalization changes society, work, education, and media. For universities, this is doubly relevant: digitalization is both a research subject and simultaneously changes daily research and teaching practice.

New research projects also show how strongly AI touches societal questions. Netzwoche reported in 2026 about a project by the University of Zurich and AlgorithmWatch CH investigating how AI influences democratic opinion formation and societal debates. Data collections, technical analyses, social science investigations, and open-source tools come together.

For research teams, this means: AI tools must not only be efficient. They must be used responsibly, transparently, and methodologically soundly. Especially with interviews, focus groups, sensitive data, and societal topics, clear processes are needed.

Swiss German as a Research Reality

Many research interviews in Switzerland do not take place in standard German.

Especially in qualitative interviews, practice projects, community projects, health studies, educational research, or surveys with local actors, participants often speak Swiss German. They often switch between dialect, standard German, technical language, English, and everyday language.

For international transcription and AI note-taking solutions, this is often difficult. Many tools recognize standard German or English well but not reliably Swiss German, dialect mixtures, local terms, or Swiss institutions.

Meeting Metrics is tailored to the Swiss market and supports Swiss German. This is especially important in research because an AI solution must not only understand individual words but also context:

1. Interview guide
2. Research question
3. Focus group
4. Practice partner
5. University
6. University of applied sciences
7. Municipality
8. Hospital
9. School
10. Administration
11. Project group
12. Research data
13. Consent
14. Anonymization
15. Coding
16. Memo
17. Pending item
18. Next steps

Switzerland and DACH: Why the Research Context Matters

Research and higher education are international but strongly locally embedded.

In Switzerland, topics such as multilingualism, federalism, data protection, research data management, ethics committees, practice relevance, universities of applied sciences projects, Swiss German, regional practice partners, and public funding play an important role.

At the same time, many research groups work in the DACH region. Then additional peculiarities arise:

1. GDPR in Germany and Austria
2. Different ethics processes
3. Different university structures
4. Different funding logics
5. Different data management requirements
6. Different terms in qualitative research
7. Cross-border project groups
8. International consortia

Meeting Metrics clearly positions itself with a Swiss focus but can also be relevant for research teams close to the DACH region who want to document German-language interviews, research meetings, and project groups more structurally.

Important: Meeting Metrics does not replace ethics review, data protection impact assessment, research data management, or methodological decisions. The solution supports transcription, structuring, and post-processing.

Typical Roles in Research and Higher Education

Meeting Metrics is not aimed at a single role. The benefit arises across various functions.

Professors

Professors conduct research meetings, doctoral supervisions, project groups, third-party funding discussions, collaborations, and committee meetings.

Meeting Metrics can help record project decisions, tasks, supervision points, and research notes more quickly.

Doctoral Candidates

Doctoral candidates conduct interviews, literature discussions, supervision sessions, colloquia, methodological discussions, and project meetings.

Meeting Metrics can prepare transcription, interview notes, supervision notes, and task lists.

Postdocs and Research Staff

Postdocs and research staff coordinate research projects, supervise students, conduct surveys, write papers, organize workshops, and collaborate with practice partners.

Meeting Metrics can structure meetings, interviews, and project groups.

Research Assistants

Research assistants transcribe interviews, prepare data, conduct literature searches, organize surveys, and support evaluations.

Meeting Metrics can particularly relieve the raw work.

Project and Study Leaders

Project leaders coordinate third-party funded projects, consortia, practice partners, deadlines, deliverables, reports, and stakeholders.

Meeting Metrics can structure project meetings, work package sessions, and status updates.

University Administration

University administrations conduct meetings on quality management, accreditation, teaching, continuing education, research support, internationalization, and digital processes.

Meeting Metrics can prepare protocols, tasks, and decisions.

Ethics Committees and Data Protection Offices

Ethics and data protection offices deal with consents, research data, risk assessments, sensitive data, and governance.

Meeting Metrics can document internal meetings but does not replace formal review.

Transfer Offices and Practice Partners

Transfer offices and practice partners work on innovation projects, applied research, practice workshops, and impact measurement.

Meeting Metrics can structurally record workshop results, decisions, and tasks.

Typical Types of Conversations and Meetings

Research Interviews

Research interviews are one of the most important use cases.

They contain statements, experiences, interpretations, examples, emotions, and contextual information.

Meeting Metrics can prepare transcripts, interview summaries, and topic overviews from them.

Typical contents include:

1. Statements of the interviewee
2. Central topics
3. Recurring motifs
4. Relevant quotes
5. Contextual information
6. Open questions
7. Follow-up topics
8. Methodological notes
9. Anonymization notes
10. Research notes

Qualitative Interviews

Qualitative interviews are often open, semi-structured, or narrative.

Meeting Metrics can help with transcription and structuring. Interpretation, coding, and theoretical classification remain with the research team.

Focus Groups

Focus groups contain multiple voices, interactions, and different perspectives.

Meeting Metrics can help structure discussions, topic blocks, positions, open questions, and next evaluation steps.

Research Meetings

Research meetings include project statuses, hypotheses, methodological questions, data issues, publication ideas, and tasks.

Meeting Metrics can create task lists, decision overviews, and research notes from them.

Project Groups

Project groups in research projects coordinate work packages, deadlines, deliverables, partner contributions, and reports.

Meeting Metrics can extract pending items, responsibilities, and next steps.

Supervision Conversations

Supervision conversations between professors, doctoral candidates, students, or research staff are often rich in feedback and tasks.

Meeting Metrics can prepare supervision notes, to-dos, open questions, and next milestones from them.

Doctoral Colloquia and Lab Meetings

Colloquia and lab meetings contain feedback, methodological notes, literature suggestions, and next steps.

Meeting Metrics can structure these points.

Practice Partner Workshops

At universities of applied sciences and in applied research, practice partner workshops are particularly important.

Meeting Metrics can record workshop results, requirements, pain points, solution ideas, and tasks.

Ethics and Data Protection Meetings

Ethics and data protection issues are central in many projects.

Meeting Metrics can prepare internal notes, open points, and tasks. Formal ethical and legal assessment remains with the responsible bodies.

Teaching Development and University Meetings

Universities hold many meetings on curricula, degree programs, accreditation, quality development, continuing education, and teaching innovation.

Meeting Metrics can structure protocols, tasks, and decisions.

Data Protection, Ethics, and Research Data

Research data can be very sensitive.

These include:

1. Interview recordings
2. Transcripts
3. Personal statements
4. Health data
5. Educational data
6. Political opinions
7. Religious or ideological information
8. Professional situations
9. Personal experiences
10. Sensitive group affiliations
11. Location data
12. Data of minors
13. Data from vulnerable groups
14. Raw data from studies
15. Contextual information that allows conclusions about persons

For universities and research teams, it is therefore important to consider:

1. Which data is collected?
2. For what purpose is it processed?
3. Who has access?
4. Where are audio, transcript, and results stored?
5. How long are data available?
6. How are participants informed?
7. Is consent given?
8. Which data must be anonymized or pseudonymized?
9. Which data must not be entered into an AI system?
10. Which projects require ethics review?
11. What requirements apply to research data management?
12. Are data used for training?
13. Which providers and subprocessors are involved?
14. Which results may be exported or shared?

Meeting Metrics supports research teams with Swiss data storage, enterprise setups with processing in Switzerland, local models, optional on-premise installation, and personal support for data protection and governance issues.

Swiss Data Processing, Local Models, and On-Premise Option

Meeting Metrics stores data in Switzerland. For enterprise customers, setups with processing in Switzerland and local models are possible.

For universities, research groups, medical research, public research projects, institutes, ethics committees, or organizations with particularly high requirements for data protection, information security, or internal IT guidelines, an individual enterprise setup up to an on-premise installation can also be considered.

This allows Meeting Metrics to be operated on-premise or in a specifically defined environment if needed.

This is especially relevant for organizations that want to document confidential research data, interview recordings, sensitive personal data, health data, minor data, or unpublished research results and require maximum control over data flows, access, processing, and operation.

Depending on the requirement, different operating models can be distinguished.

Swiss Data Storage

Data is stored in Switzerland. This is already an important basic requirement for many Swiss universities, research groups, and public projects.

Swiss Processing in Enterprise Setup

For more sensitive research projects, processing in Switzerland can also be relevant.

Local Models

For particularly high requirements, local models can be considered to gain more control over processing.

On-Premise Installation

For universities, research groups, or public research organizations with particularly high requirements, an on-premise installation can be considered. This allows the solution to be operated in the organization's own or a specifically defined infrastructure.

Recording Sources: Interview, Focus Group, Project Meeting, or Upload

Meeting Metrics is flexible and not limited to a single recording type.

Research teams work in interview rooms, online, hybrid, in the field, in labs, at workshops, in project groups, or with existing audio and video files. Meeting Metrics can start exactly there.

Mobile App for Interviews and Field Research

Many interviews and observations take place physically or on the go.

The mobile app allows the smartphone to be used for recordings. This is practical because the smartphone is almost always with you anyway.

This allows interviews, short field notes, reflection memos, practice conversations, or workshop notes to be recorded directly and then processed.

Desktop App for Online Interviews and Research Meetings

For Teams, Zoom, Google Meet, or other online meetings, Meeting Metrics can be used via the desktop app.

The recording runs in the background without a visible bot joining the meeting. This is especially convenient for interviews, confidential research meetings, or project groups.

Upload of Audio and Video Files

Existing audio or video files can be uploaded.

This is useful if recordings already exist, for example from interviews, focus groups, video conferences, dictation devices, or workshop recordings.

Meeting Metrics then transforms these recordings into structured transcripts, summaries, tasks, research notes, or reports.

Smartphone as Research Note Tool

A separate recording device is not necessary.

With the smartphone, short observations, reflections, interview notes, or project thoughts can be recorded directly. Meeting Metrics then creates structured notes, tasks, or memos from them.

Research and University Software: Where Meeting Metrics Could Integrate Results

Swiss universities and research teams work with many different systems. Meeting Metrics does not replace these systems. The added value lies in transforming interviews and meetings into structured results that can then be transferred to existing research, DMS, project management, analysis, or university systems.

Depending on the setup, transcripts, summaries, pending items, tasks, reports, or research notes can be exported or transferred via interfaces to existing systems.

MAXQDA, ATLAS.ti, and NVivo

Many qualitative research projects use tools like MAXQDA, ATLAS.ti, or NVivo for coding, qualitative content analysis, and evaluation.

Meeting Metrics does not replace these systems. However, the solution can prepare transcripts and structured interview notes that can then be imported into qualitative analysis tools.

Possible exports or integrations:

1. Transcript
2. Interview summary
3. Topic overview
4. Relevant quote passages
5. Method memo
6. Anonymization notes
7. Coding preparation
8. Research note

REDCap

Many universities and medical research groups use REDCap for research data, surveys, and study management.

Meeting Metrics can help complementarily to transfer interviews, study meetings, or project groups into structured notes and tasks. Transfer into study systems should be controlled and reviewed.

LimeSurvey, Qualtrics, and Survey Tools

Survey tools like LimeSurvey or Qualtrics are often used for surveys and mixed-methods projects.

Meeting Metrics can document qualitative accompanying interviews, focus groups, or evaluation sessions.

SWITCHdrive, SWITCHengines, and University Clouds

Many Swiss universities use SWITCH-related services or their own university clouds for data, collaboration, and infrastructure.

Meeting Metrics can export results so that they can be integrated into existing storage structures.

Moodle, ILIAS, OLAT, and Learning Platforms

Universities use learning platforms for teaching, course organization, feedback, and materials.

Meeting Metrics can structure teaching development meetings, feedback rounds, project groups, or supervision conversations. Direct integration should be reviewed depending on university IT.

Microsoft 365, Teams, SharePoint, and Outlook

Many universities and research teams use Microsoft 365 for email, calendar, Teams meetings, documents, storage, and collaboration.

Meeting Metrics can interact with existing workflows here, for example via:

1. Exports
2. Email
3. Share Links
4. Filing Processes
5. Teams Meetings
6. Tasks
7. Future Integrations

Zotero, Mendeley and Reference Management

Reference management systems remain central for source work. Meeting Metrics does not replace these systems but can structure research meetings and literature discussions.

Jira, Confluence, Notion, Trello, Asana and Project Tools

Research groups and university projects often use project and knowledge management tools.

Meeting Metrics can structure project meetings, work package sessions, practice partner workshops, or research group meetings and prepare tasks.

DMS, Repositories and Research Data Management

Many universities work with DMS, institutional repositories, or research data management platforms.

Meeting Metrics can structure content that can then be properly archived, anonymized, or transferred to project folders.

How an Integration with Meeting Metrics Can Look in Practice

An integration does not always have to be a deep technical interface immediately. Especially in universities and research groups, a step-by-step approach is often more sensible.

Stage 1: Structured Export

Meeting Metrics creates a structured result that is further processed as Word, PDF, text, or via a share link.

This is suitable for research teams that first want to check quality and do not want to change existing processes immediately.

Stage 2: Template-Based Transfer

Meeting Metrics creates results directly in the structure the research team needs.

For example:

1. Research Interview
2. Focus Group
3. Interview Summary
4. Method Memo
5. Project Meeting
6. Lab Meeting
7. Supervision Meeting
8. Practice Partner Workshop
9. Ethics Discussion
10. Study Coordination
11. Management Update
12. Pending Items List

This reduces copy-paste and improves consistency.

Stage 3: API or Interface Connection

For larger institutes, research centers, or universities, Meeting Metrics can be connected via API or defined interfaces.

Thus, verified transcripts, summaries, tasks, or research notes could be transferred to existing DMS, analysis, project management, or university systems.

Important: Especially with research data, interviews, and sensitive personal data, the transfer should be controlled. The responsible person reviews and confirms the content before it is transferred to a research system, repository, or project file.

Stage 4: Enterprise Setup with Swiss Processing or On-Premise

For particularly sensitive research data, an enterprise setup with Swiss processing, local models, or on-premise installation can be considered.

This is especially relevant for:

1. Health Research
2. Clinical Studies
3. Educational Research with Minors
4. Social Research with Vulnerable Groups
5. Confidential Practice Partner Projects
6. Public Research Mandates
7. Ethics-Required Studies
8. Security-Relevant Research
9. Unpublished Research Results
10. Large University or Institute Rollouts

Where Meeting Metrics Can Specifically Support

Meeting Metrics can create added value for research and universities on several levels.

Transcribe Interviews Faster

Transcripts arise from audio or video recordings that researchers can further process.

Prepare Interview Summaries

Meeting Metrics can structure core topics, statements, open questions, and follow-up points.

Document Research Meetings

Project decisions, tasks, methodological questions, and open points become available faster.

Manage Project Groups

Work packages, responsibilities, deadlines, and deliverables are recorded more clearly.

Structure Supervision Meetings

Supervision meetings can be documented with tasks, feedback, and next milestones.

Process Practice Partner Workshops

Workshops with companies, administrations, schools, hospitals, or municipalities can be converted into clear result protocols.

Make Research Knowledge Findable

Through AI chat and search, previous meetings can be queried more easily.

Examples:

1. What was decided about the method in the last research meeting?
2. Which open points exist from the interviews?
3. Which topics appeared multiple times in the focus groups?
4. Which tasks are with the project management?
5. Which questions must be addressed to the ethics committee?
6. Which interview statements concern data protection?
7. Which follow-up questions should be asked in the next survey?
8. What was agreed as the next milestone in the supervision meeting?
9. Which practice partner pending items are open?

Use Cases for Research and Universities

1. Transcribe and Structure Qualitative Interviews

Meeting Metrics can transcribe interviews and prepare initial summaries, topic overviews, and research notes.

2. Document Focus Groups

Focus groups can be structured into topic blocks, positions, open questions, and next evaluation steps.

3. Post-Process Research Meetings

Project decisions, methodological questions, tasks, and deadlines are documented more clearly.

4. Coordinate Project Groups

Research teams can better track work packages, responsibilities, and deliverables.

5. Record Supervision Meetings

Doctoral candidates, students, and supervisors can document feedback, tasks, and milestones more transparently.

6. Evaluate Practice Partner Workshops

Workshops with practice partners can be quickly converted into result protocols, tasks, and reports.

7. Prepare Ethics and Data Protection Discussions

Open points, responsibilities, and next steps can be recorded in a structured way.

8. Relieve University Administration and Committee Work

Faculty meetings, institute meetings, teaching development workshops, and project meetings can be documented more efficiently.

Economic Benefit: What Meeting Metrics Specifically Brings to a Research Group

The economic benefit of Meeting Metrics does not arise from replacing researchers but from better support in everyday work.

Professors, doctoral candidates, postdocs, scientific staff, research assistants, and project leaders remain professionally and scientifically responsible. However, Meeting Metrics helps reduce repetitive post-processing, prepare transcripts faster, capture tasks more structurally, and find research knowledge better.

This is especially important in research and universities because many teams simultaneously have to manage tight budgets, third-party funding requirements, publication pressure, project deadlines, teaching obligations, and data management requirements.

Example Calculation: Research Group with Qualitative Interviews

Let's take a research group with 6 to 10 people, several work packages, and 40 qualitative interviews.

Typical conversation and meeting types in the project:

1. Qualitative Interviews
2. Focus Groups
3. Research Meetings
4. Supervision Meetings
5. Practice Partner Workshops
6. Method Sessions
7. Ethics and Data Protection Discussions
8. Project Group Meetings
9. Evaluation Rounds
10. Report Meetings

In practice, this can quickly result in 60 to 120 documentation-relevant conversations in a project. Especially interviews and focus groups generate a lot of post-processing.

If several hours per interview are currently spent on transcription, structuring, and initial preparation, this results in significant effort. Research meetings and project groups also continuously cause post-processing.

Meeting Metrics does not completely eliminate this effort. That would not be the right claim. Scientific review, anonymization, methodological analysis, coding, and interpretation remain with the research team.

But the starting point improves significantly.

Instead of starting from scratch, teams receive a structured basis:

1. Transcript
2. Interview Summary
3. Topic Overview
4. Research Note
5. Follow-up Questions
6. Task List
7. Open Points
8. Method Memo
9. Project Status
10. Searchable Research Knowledge

If a team saves on average only 60 minutes of raw work per interview for 40 interviews, this results in 40 hours of relief.

If additionally 20 minutes of post-processing are saved on average for 30 research meetings, another 10 hours are added.

This time can be used where it is more valuable:

1. Scientific Analysis
2. Coding
3. Interpretation
4. Publication
5. Project Coordination
6. Supervision
7. Method Discussion
8. Practice Partner Communication
9. Data Management
10. Quality Assurance

More Quality Instead of Just Less Effort

The benefit is not only reflected in hours.

Meeting Metrics can also improve the quality of post-processing:

1. Transcripts are available faster.
2. Project decisions are documented more clearly.
3. Tasks are better assigned.
4. Open research questions are less likely to be lost.
5. Follow-up questions become more visible.
6. Practice partner workshops lead to result protocols faster.
7. Supervision meetings become more comprehensible.
8. Research knowledge remains findable.
9. New team members find context faster.

The economic benefit is therefore not in automating research. It lies in relieving researchers from repetitive raw work and creating more time for methodologically and scientifically demanding work.

Everyday Life in Research and Universities: Why Time Savings Matter

Research teams are under pressure.

Third-party funded projects have tight schedules. Doctoral candidates must collect, analyze, and publish data. Professors juggle research, teaching, supervision, committees, and collaborations. Universities of applied sciences often work with practice partners who expect quick and usable results.

At the same time, the importance of AI is growing at Swiss universities. Netzwoche reported in 2026 that ETH Zurich and EPFL continue to hold top positions in computer science and AI in international rankings. This shows how strongly Switzerland is positioned in AI research – and how relevant AI skills are becoming in everyday university life.

Meeting Metrics relieves where particularly much manual work arises: in interviews, transcripts, research meetings, project groups, supervision meetings, and workshops.

According to an internal user survey from Q1 2026 with 100 Meeting Metrics users, Meeting Metrics saves on average about 25 minutes per meeting hour. For interviews and longer research discussions, the relief can be significantly higher depending on the starting point.

For research and universities, this means: less time for raw transcripts and post-processing – more time for analysis, teaching, publication, and impact.

Which Conversations Are Particularly Suitable?

Meeting Metrics is particularly suitable for situations where a concrete result is needed after the conversation.

These include:

1. Research interviews
2. qualitative interviews
3. focus groups
4. expert discussions
5. research meetings
6. project groups
7. supervision meetings
8. doctoral colloquia
9. lab meetings
10. practice partner workshops
11. ethics discussions
12. data protection discussions
13. study coordinations
14. work package meetings
15. university committees
16. teaching development workshops
17. faculty meetings
18. institute meetings

Less suitable are conversations in which particularly sensitive data would be processed without clear consent, without a defined purpose, or without an agreed governance concept. Especially health data, data of minors, interviews with vulnerable groups, confidential practice partner data, or ethically sensitive studies should be examined particularly carefully.

Why research does not need a generic AI notetaker solution

Many AI notetakers are built for sales, recruiting, or remote teams.

Research and universities have different requirements.

A sales team might need conversation summaries and CRM updates. A research team needs transcripts, interview guides, anonymization, research notes, method memos, coding preparation, ethics references, project tasks, and controlled storage.

An international tool might understand “meeting summary.” But a Swiss research group needs:

1. research interview
2. qualitative study
3. focus group
4. research question
5. interview guide
6. transcript
7. anonymization
8. consent
9. research data management
10. ethics committee
11. method memo
12. coding
13. practice partner
14. project group
15. work package
16. deliverable
17. Swiss German
18. DACH context
19. next steps

Meeting Metrics is therefore not just an AI notetaker, but a solution that can be adapted to the working methods of Swiss universities, universities of applied sciences, and research groups.

Introduction to research teams and universities: test first, then roll out cleanly

The use of AI in research and universities should be pragmatic and controlled.

Therefore, a clearly defined entry point is recommended. Research teams, institutes, or university departments can first test Meeting Metrics in a pilot, assess the quality, and then decide how to design a rollout sensibly.

1. Test Enterprise for two weeks

A research team can initially test Meeting Metrics for two weeks in the Enterprise setup. This phase is not about immediately switching all research data. It is about realistically checking the quality.

Typical tests can be:

1. a research meeting
2. an internal project meeting
3. an anonymized test interview
4. a non-sensitive expert discussion
5. a practice partner workshop
6. a supervision meeting
7. a lab meeting
8. a methods session
9. a dictation for a research memo

This way, the team quickly sees how well transcription, Swiss German, summarization, tasks, research notes, and templates work.

2. Convince with quality

After the first tests, it can be checked:

1. How well does Meeting Metrics understand Swiss German?
2. How good is the transcript quality?
3. How useful is the interview summary?
4. How well are tasks recognized?
5. How well are open research questions recognized?
6. How well do the results fit the own methodology?
7. Which templates need to be adjusted?
8. Which types of conversations are particularly suitable?
9. Which types of conversations should be consciously excluded?
10. How well does the review process by researchers work?
11. How well can results be integrated into existing research systems?

This phase is important so that Meeting Metrics is not evaluated theoretically, but based on real research and university contexts.

3. Optimize templates together

Afterwards, the most important templates can be adjusted.

For example:

1. research interview
2. expert discussion
3. focus group
4. interview summary
5. method memo
6. research meeting
7. lab meeting
8. supervision meeting
9. practice partner workshop
10. ethics discussion
11. study coordination
12. project group
13. management update

The better the template, the better the result. Meeting Metrics supports research teams in adopting existing workflows and preparing them sensibly for AI.

4. Clarify data protection, ethics, and operating model

In parallel, it should be clarified which data protection, ethics, and IT requirements apply.

These include:

1. data processing agreement
2. technical and organizational measures
3. subprocessors
4. data location
5. Swiss processing
6. retention and deletion
7. access rights
8. consent
9. research data management
10. anonymization
11. pseudonymization
12. use of data for training
13. ethics requirements
14. operating model
15. local models
16. on-premise option
17. integration into research, analysis, or DMS systems

For particularly sensitive research data, it can be checked whether an Enterprise setup with Swiss processing, local models, or on-premise installation makes sense.

5. On-site onboarding

For universities and research teams, personal onboarding is particularly valuable.

Meeting Metrics can conduct on-site onboarding if needed. This is not only about operating the software but also about practical tips from everyday research.

For example:

1. Which types of conversations are suitable for the start?
2. How to properly inform interview participants?
3. How to start and stop recordings correctly?
4. How to use interview guides and context files?
5. How to create better research templates?
6. How to efficiently check AI results?
7. How to organize team folders and access rights?
8. How to avoid unnecessary data retention?
9. How to work with tasks, pending items, and AI chat?
10. How to separate raw data from analysis notes?
11. Which content should not be recorded?
12. How can results be integrated into analysis or project systems?

The goal is not only to show the software. The goal is for research teams to quickly find a secure, accepted, and meaningful working process.

6. Gradual rollout

After the pilot, Meeting Metrics can be rolled out step by step.

A sensible order can be:

1. first internal research meetings and dictations
2. then non-sensitive expert discussions
3. then selected interviews
4. then focus groups or practice partner workshops
5. later exports or interfaces to existing systems
6. for particularly sensitive projects only after additional ethics and data protection review

This keeps the introduction controlled and the team can gain experience before additional areas are added.

Example: This is how a workflow for a research interview could look

Before the conversation, the research team uploads the interview guide, study objective, or relevant context information.

During the interview, Meeting Metrics runs in the background via the desktop app or, for physical interviews, via the mobile app, provided consent, purpose, and data protection are properly regulated.

After the interview, Meeting Metrics creates a transcript, a structured summary, key topics, open follow-up questions, and possible research notes.

The researchers review the result, anonymize sensitive information, and decide which content is included in the analysis.

Later, the team can ask via the AI chat:

1. Which topics were mentioned multiple times in the interview?
2. Which statements directly relate to the research question?
3. Which follow-up questions arise?
4. Which information must be anonymized?
5. Which points are suitable for the next evaluation round?

Example: This is how a workflow for a research meeting could look

A project group discusses the status of a third-party funded project, open work packages, and methodological questions.

Meeting Metrics then creates a structured summary with decisions, tasks, responsibilities, deadlines, and open questions.

The project management reviews the result and incorporates relevant points into the project plan, the DMS, or the reporting.

Typical results are:

1. project status
2. task list
3. responsibilities
4. open methodological questions
5. deadlines
6. deliverables
7. next steps
8. management update

Example: This is how a workflow for a practice partner workshop could look

A research team conducts a workshop with a municipality, a hospital, a school, or a company.

Meeting Metrics then creates a summary with needs, pain points, solution ideas, tasks, open questions, and next steps.

The team reviews the result and uses it for reports, minutes, project planning, or communication with practice partners.

Benefits for Swiss Universities and Research Teams

Less Manual Transcription and Post-Processing Work

The AI prepares transcripts, summaries, tasks, and research notes. This saves time in post-processing.

Support Instead of Replacement

Meeting Metrics supports researchers, project managers, doctoral candidates, and university administrations with the raw work. The scientific analysis, methodological quality, and final responsibility remain with the team.

Better Conversation Quality

Researchers can focus more on the interview, interlocutors, method, and content instead of simultaneously taking notes.

More Traceability

Decisions, tasks, open questions, and methodological discussions are recorded more structurally.

Faster Availability

Transcripts, research notes, and project minutes can be made available more quickly.

Swiss German Support

Meeting Metrics is tailored to the Swiss market and also understands Swiss German.

DACH-Compatible Research Documentation

For projects related to Switzerland, Germany, and Austria, interviews, project meetings, and research groups can be documented more structurally.

Swiss Data Hosting and On-Premise Option

Data is stored in Switzerland. For enterprise customers, setups with processing in Switzerland, local models, or an on-premise installation are possible. This is especially relevant for sensitive research data, health data, minor data, or confidential practice partner projects.

Custom Templates

Research teams can use their own templates for interviews, focus groups, method memos, project meetings, or supervision discussions.

Personal Support

Introduction, rollout, templates, data protection questions, ethical issues, and integration options can be clarified together.

On-Site Onboarding

If needed, Meeting Metrics can accompany universities and research teams on-site and provide practical tips for interview processes, templates, data protection, recording processes, exports, and internal introduction.

No Visible Bots

Meeting Metrics runs in the background via desktop or mobile app. This is especially pleasant for confidential interviews and research meetings.

No Additional Recording Device Needed

The desktop app is sufficient for online meetings. For interviews, field research, dictations, or spontaneous notes, the smartphone with the mobile app is enough.

Which Universities and Research Teams is Meeting Metrics Suitable For?

Meeting Metrics is suitable for universities, universities of applied sciences, teacher training colleges, institutes, research groups, doctoral programs, labs, transfer offices, university administrations, public research projects, practice research projects, evaluation teams, and DACH research consortia.

The solution is particularly relevant for organizations that:

1. regularly transcribe interviews
2. conduct qualitative research
3. document focus groups
4. want to post-process research meetings more efficiently
5. coordinate project groups
6. want to structure supervision discussions
7. document practice partner workshops
8. use Swiss German
9. have DACH projects or international consortia
10. have high requirements for data protection and ethics
11. want to continue using existing research and university systems
12. do not want additional recording hardware
13. do not want visible bots in conversations
14. want to make research knowledge from past conversations more easily findable

Frequently Asked Questions from Research Teams and Universities

Is Meeting Metrics Suitable for Research?

Yes. Meeting Metrics is suitable for research teams that want to transcribe and structure interviews, focus groups, research meetings, project groups, or practice partner workshops.

Is Meeting Metrics Suitable for Universities?

Yes. Meeting Metrics is suitable for universities, universities of applied sciences, teacher training colleges, institutes, and university administrations that want to document meetings, interviews, and project work more efficiently.

Does Meeting Metrics Support Qualitative Interviews?

Yes. Meeting Metrics can transcribe qualitative interviews and prepare summaries, topic overviews, research notes, and tasks from them.

Does Meeting Metrics Replace Qualitative Analysis?

No. Meeting Metrics does not replace scientific analysis, coding, interpretation, or methodological responsibility. The solution creates a structured basis and supports post-processing.

Does Meeting Metrics Support Focus Groups?

Yes. Meeting Metrics can transcribe focus groups and structure them into topics, positions, open questions, and next evaluation steps.

Can Meeting Metrics Understand Swiss German?

Yes. Meeting Metrics is tailored to the Swiss market and also supports Swiss German. This is especially important for interviews, practice projects, and field research in Switzerland.

Is Meeting Metrics Also Suitable for DACH Projects?

Yes. Meeting Metrics can also help with DACH-related research projects, such as interviews and project meetings with partners in Germany and Austria. The data protection and ethical assessment remains with the responsible local authorities.

Where Are the Data Stored?

Meeting Metrics stores data in Switzerland. For enterprise customers, setups with processing in Switzerland, local models, or an on-premise installation are possible. This allows research teams to choose an appropriate operating model depending on data protection requirements, IT strategy, and data sensitivity.

Is an On-Premise Installation Possible?

Yes. For universities, research institutes, or projects with particularly high requirements for data protection, information security, or internal IT guidelines, Meeting Metrics can be considered as an individual enterprise setup with on-premise installation.

Is the Use of AI in Research Interviews Data Protection Compliant?

In principle, use can be possible if purpose, consent, proportionality, order processing, access, information obligations, retention, deletion, and data security are properly regulated. Particularly sensitive projects require careful examination.

Must Every Interview Be Recorded?

No. Research teams should consciously define for which interview types Meeting Metrics is used and for which it is not. Particularly sensitive interviews require clear governance.

Are Data Used for Training?

For research, it is crucial that interview and research data are not used for model training. This should be contractually and technically clarified in the specific setup.

Can a Research Group Test Meeting Metrics First?

Yes. Research teams can initially test Meeting Metrics in the enterprise setup, for example for two weeks. During this phase, internal meetings, test interviews, non-sensitive interviews, or dictations can be processed, templates checked, and quality assessed.

Is On-Site Onboarding Available?

Yes. On-site onboarding is possible for universities and research teams. This includes not only explaining the functions but also providing practical tips on interviews, templates, data protection, consent, recording processes, exports, and internal introduction.

Does Meeting Metrics Also Work for Physical Interviews?

Yes. Meeting Metrics can also be used for physical interviews, focus groups, field research, dictations, or on-site meetings via the mobile app.

Does Meeting Metrics Work with Microsoft Teams or Zoom?

Yes. Meeting Metrics can be used for online interviews and research meetings, among others via the desktop app. The solution is not limited to a single meeting platform.

Can Meeting Metrics Be Connected to MAXQDA, ATLAS.ti, or NVivo?

Meeting Metrics does not replace these systems. However, the solution can structure and export transcripts, summaries, and research notes. Depending on the setup, interfaces or exports into qualitative analysis processes are conceivable.

Are Contents Automatically Written into Research Systems?

Not without review. Especially with research data and personal data, it is important that researchers check, anonymize, and approve content before it is transferred into analysis, DMS, or repository systems.

Can Meeting Metrics Assign Tasks to Project Members?

Yes. Meeting Metrics can extract tasks, to-dos, and responsibilities from research meetings or project groups. These can be exported or integrated into existing task and project management processes depending on the setup.

Is Deep Technical Integration Immediately Necessary?

No. Many research teams start with structured exports, templates, and manual adoption. Afterwards, it can be assessed whether an API or interface integration makes sense.

Can Meeting Metrics Use Different Research Templates?

Yes. Research teams can work with individual templates, for example for interviews, focus groups, method memos, lab meetings, supervision discussions, project groups, or practice partner workshops.

Does Meeting Metrics Require Additional Hardware?

No. There is a desktop app for online meetings. For interviews, field research, physical meetings, or spontaneous notes, the mobile app can be used. Research teams do not need to buy or manage additional recording devices.

How Quickly Does a Research Team See the Benefits?

Often, a short pilot with real meetings, test interviews, or selected interviews is enough to assess quality and time savings. Research meetings, practice partner workshops, qualitative interviews, focus groups, or supervision discussions are particularly suitable.

Conclusion: AI Minutes Must Fit the Research Routine

Swiss universities and research teams do not need a generic AI notetaker solution.

They need a solution that understands the research routine:

1. Research interviews
2. Qualitative interviews
3. Focus groups
4. Research meetings
5. Project groups
6. Supervision discussions
7. Lab meetings
8. Practice partner workshops
9. Ethics
10. Data protection
11. Research data management
12. Swiss German
13. DACH peculiarities
14. Existing research systems

Meeting Metrics helps research teams document conversations more efficiently, prepare transcripts faster, record tasks better, and make knowledge from conversations usable in the long term.

The most important difference is not in transcription.

It's about what happens after the conversation.

With Meeting Metrics, interviews, research meetings, and project groups become structured transcripts, research notes, tasks, to-dos, and reusable research knowledge. For particularly sensitive research data, in addition to Swiss data hosting and Swiss processing, local models and on-premise setups are also possible.

Secure, structured, and suitable for Swiss universities, universities of applied sciences, and DACH-related research teams.

Ready for Less Post-Processing in Research and Academia?

With Meeting Metrics, you can focus more on research, analysis, teaching, supervision, and impact again. Transcripts, conversation notes, minutes, tasks, and summaries are created automatically in the background.

Whether research interview, focus group, project meeting, supervision discussion, lab meeting, practice partner workshop, or physical on-site conversation: Meeting Metrics runs via desktop and mobile apps on the devices you already use.

Universities and research teams can test Meeting Metrics for two weeks in the enterprise setup, evaluate real conversations, be convinced by the quality, and then gradually introduce it. On-site onboarding is available on request – including tips on interview templates, research notes, data protection, consent, recording processes, exports, integrations, and rollout.

For particularly sensitive research data, Swiss processing, local models, or an on-premise installation can be considered.

Test Meeting Metrics for free or arrange a demo for your research team.

Questions? Write to us at info@meetingmetrics.ai.

Current Swiss Sources Used in This Blog

Swissinfo, “Swiss universities to release multilingual AI programme”, July 9, 2025

The article was used in the section on the importance of AI in Swiss research and universities. It shows that Swiss universities are developing a multilingual AI model on public infrastructure, combining research, multilingualism, and trustworthiness.

https://www.swissinfo.ch/eng/swiss-ai/swiss-universities-to-releaselarge-language-model/89655364

Inside IT, “ETHs publish Swiss AI language model”, September 2, 2025

The article was used in the section on Apertus and Swiss AI infrastructure. It describes the release of the open Swiss language model by ETH Zurich, EPFL, and CSCS, as well as possible applications in education, research, society, and economy.

https://www.inside-it.ch/eths-veroeffentlichen-schweizer-ki-sprachmodell-20250902

SRF, “Digitization: What Does Digital Transformation Do to Society?”, 2026

The article was used in the section on the digital transformation of research, education, and society. It fits content-wise because it shows that digitization is not just a technical issue but changes research, education, work, and social processes.

https://www.srf.ch/news/gesellschaft/digitalisierung-was-macht-die-digitale-transformation-mit-der-gesellschaft

Netzwoche, “UZH and AlgorithmWatch investigate what AI does to society”, March 19, 2026

The article was used in the section on AI as a research subject. It shows that Swiss research does not only consider AI technically but also examines social impacts, opinion formation, and democratic debate.

https://www.netzwoche.ch/news/2026-03-19/uzh-und-algorithmwatch-untersuchen-was-ki-mit-der-gesellschaft-macht

Netzwoche, “ETH Zurich and EPFL Assert Leading Position in Computer Science and AI”, March 27, 2026

The article was used in the section on the Swiss university and research location. It supports the statement that Swiss universities are internationally well positioned in computer science and AI.

https://www.netzwoche.ch/news/2026-03-27/eth-zuerich-und-epfl-behaupten-spitzenposition-in-informatik-und-ki