Training course · Vibe Coding · Research & Science

Vibe Coding for Researchers

Build software in natural language: from prompt to app, rapid iteration, quality and security checks, and turning prototypes into production.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Vibe Coding for Researchers” is a hands-on course for roles such as Doctoral candidates, Research associates and postdocs and Professors and research group leaders. Rather than learning Vibe Coding in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Research & Science.

Vibe coding lets business users without any programming knowledge build digital tools of their own. You describe in everyday language what a mini app, dashboard or form should do, and tools such as Lovable, Claude, ChatGPT Canvas or Replit generate a working solution. Helpers for your daily work can take shape quickly, without waiting for IT to free up capacity. This course shows you step by step how to make it work, which habits lead to good results, and what really matters along the way.

During the course you build applications drawn from your own work: a KPI overview, a data entry form, a small calculator or an automation for recurring chores. At the same time you learn to assess the risks realistically. Which data may go into such tools, who will run and maintain the solution, and when must IT be involved? With this knowledge you can create useful solutions that are accepted within the organisation and remain secure and sustainable in the long term.

Academic work is shaped by literature reviews, data analysis, grant writing and publication pressure. Generative AI can help at every stage: navigating and summarising the literature, sharpening research questions, writing analysis code, polishing manuscripts and preparing funding applications. AI agents can automate recurring steps such as data cleaning or cross-checking references. At the same time, AI touches core questions of research integrity: who is an author, how is AI use disclosed, how do you prevent fabricated references? Researchers therefore need more than prompting tricks – they need a methodologically sound, transparent and reproducible way of working with AI tools.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Doctoral candidates
  • Research associates and postdocs
  • Professors and research group leaders
  • Research offices and grant managers
  • Corporate R&D teams

Typical challenges we address

  • An ever-growing body of literature that is hard to keep up with
  • Heavy writing load for proposals, reports and papers
  • Data analysis that requires programming skills
  • Uncertainty about AI rules set by journals, funders and universities

What you will be able to do

  • You build your own mini apps, dashboards and forms through vibe coding without programming skills.
  • You describe requirements clearly enough for AI tools to produce usable solutions.
  • You improve your applications step by step and check the results for accuracy.
  • You automate simple recurring tasks in your working environment.
  • Use AI for literature work and writing without breaching research integrity standards.
  • Produce traceable analysis code with AI support and document it reproducibly.
  • Disclose AI use transparently in line with journal, funder and institutional requirements.

Course outline

Day 1

Module 1 Understanding vibe coding as a business user
  • What vibe coding is and what you can achieve without programming skills
  • Tools at a glance: Lovable, Claude, ChatGPT Canvas, Replit and similar platforms
  • Spotting good candidates for your own tools at work
  • Realistic expectations: strengths, limits and common pitfalls
Module 2 Describing and building your own mini app
  • Capturing goal, users, inputs and desired outcome in plain words
  • Generating a first version and clicking through it together
  • Improving look, wording and flow with targeted feedback
  • Saving milestones and returning to an earlier version when things go wrong
Module 3 Dashboards, forms and calculators
  • Turning Excel or CSV data into an interactive dashboard
  • Building entry forms with required fields and plausibility checks
  • Creating calculators and decision aids for your team
  • Checking results for accuracy with test data and spot checks
Module 4 Automating routine work
  • Identifying recurring tasks that lend themselves to automation
  • Building simple automations for reports, notifications or file storage
  • Discussing connections to existing tools via approved interfaces
  • Documenting automations clearly and handing them over to colleagues

Day 2

Module 5 Risks and coordination with IT
  • Which data may and may not go into AI tools: data protection and confidentiality
  • Managing access rights, sharing settings and public links deliberately
  • Avoiding shadow IT: clarifying operation, maintenance and ownership
  • Preparing a handover to IT when a solution grows or becomes critical
Module 6 Hands-on project: your own solution for Research & Science
  • Literature matrix – A small app for logging studies with method, sample and key finding, filtering them and exporting a table for the state-of-the-art section.
  • Data dashboard – An internal tool that reads measurement data from CSV files, runs plausibility checks and displays trends and outliers interactively. Publication-ready exports are included.
  • Proposal checker – A tool that compares a draft proposal against a stored checklist of funding requirements and flags missing mandatory sections or exceeded page limits.
Module 7 Data protection, quality & transfer to daily work
  • Codes of research integrity apply: AI cannot be an author, its use must be disclosed and all references must be checked against the original. Personal research data, for example from studies involving participants, is subject to the GDPR and ethics approvals; unpublished results and peer reviews must be treated as confidential.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Navigating the literature

AI summarises abstracts and full texts, extracts methods, samples and findings into comparison tables and helps formulate search strings for scholarly databases – every source is then verified in the original.

Sharpening research design

Research questions, hypotheses and operationalisations are stress-tested with AI as a critical sparring partner to spot gaps, confounders and alternative explanations early. Control conditions and limits to generalisability are discussed as well.

Writing analysis code

Researchers without deep programming skills use AI to produce Python or R scripts for data cleaning, statistics and visualisation, documenting them clearly to ensure reproducibility.

Revising manuscripts

AI supports language editing, structuring and shortening of academic texts, checks terminology for consistency and helps draft responses to reviewers without taking over authorship. Your argument stays your own.

Preparing grant proposals

Funding guidelines are turned into checklists, work packages and milestones are structured, and sections such as impact plans or lay summaries are drafted in several versions.

Analysing qualitative data

Interview transcripts and open-ended responses are pre-coded and grouped thematically with AI; researchers review, refine and document the coding frame according to established methodology. Inter-coder checks remain essential.

Possible hands-on projects

Literature matrix

A small app for logging studies with method, sample and key finding, filtering them and exporting a table for the state-of-the-art section.

Data dashboard

An internal tool that reads measurement data from CSV files, runs plausibility checks and displays trends and outliers interactively. Publication-ready exports are included.

Proposal checker

A tool that compares a draft proposal against a stored checklist of funding requirements and flags missing mandatory sections or exceeded page limits.

Prerequisites

No programming knowledge is required; confident use of a browser, office applications and spreadsheets is enough. Experience of academic work in a discipline; programming skills are helpful but not required.

Technical requirements

Your own laptop with an up-to-date browser, Git and an AI coding tool of your choice, such as an AI editor or coding agent with an active account.

Data protection & compliance

Codes of research integrity apply: AI cannot be an author, its use must be disclosed and all references must be checked against the original. Personal research data, for example from studies involving participants, is subject to the GDPR and ethics approvals; unpublished results and peer reviews must be treated as confidential.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Berlin Request
  • Tue, 15 December 2026 – Wed, 16 December 2026 Online Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Stuttgart Request
  • Thu, 4 February 2027 – Fri, 5 February 2027 Online Request
  • Thu, 18 February 2027 – Fri, 19 February 2027 Hanover Request

Frequently asked questions

Is vibe coding only suitable for quick prototypes?

Vibe coding shines with prototypes, internal tools and experiments. With the right practices, namely clear specifications, tests, reviews and sound version control, results can also be turned into production-ready applications. The course teaches you to manage that transition deliberately and to recognise early on when rebuilding is the wiser option.

Which tools are used in the course?

The course is vendor-neutral. We work with the main tool categories: AI editors such as Cursor or Windsurf, coding agents such as Claude Code or Codex, and browser-based app builders. You are welcome to use your preferred tool, as the principles of specification, iteration, quality and security apply to all of them.

How should I deal with security risks in generated code?

Generated code often contains typical weaknesses such as hard-coded credentials, missing input validation or overly open interfaces. You learn to check specifically for these patterns, build security requirements into the specification from the start, use automated scanning and require a deliberate human sign-off before every deployment.

Can I use AI to write my PhD thesis?

That depends on your doctoral regulations and institutional policies. AI is often permitted for language editing and brainstorming if its use is disclosed and your own intellectual contribution remains clear. In the course you learn to document use properly and assess common grey areas with confidence.

How do I avoid fabricated references?

Language models can produce plausible-sounding citations that do not exist. Work with tools that query real databases, ask for DOIs and verify every source in the original. The course demonstrates a workflow that combines AI-assisted searching with reliable reference management.

How much does “Vibe Coding for Researchers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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