Training course · Lovable · Research & Science

Lovable for Researchers

Build web apps from prompts: take Lovable from idea to a working React app with Supabase backend, GitHub sync and deployment.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Lovable for Researchers” is designed for roles such as Doctoral candidates, Research associates and postdocs and Professors and research group leaders and shows step by step how to use Lovable productively, safely and transparently in Research & Science – from the fundamentals to advanced workflows.

Lovable lets you build web applications simply by describing what you need, with no programming knowledge required. In this course, business users from marketing, sales, HR, operations or product management learn how to turn an idea into a clickable, working application within hours: a landing page for a campaign, an internal tool for logging requests, a simple customer portal or a prototype that helps you pitch an idea convincingly inside your organisation.

The course is hands-on throughout. You work on a small project of your own and learn to state requirements clearly, store data securely, set up logins and publish the result. Just as important is a realistic perspective: which projects you can deliver without IT support, when to involve IT or your data protection officer, and how to hand a successful prototype over to a development team. That way you use Lovable productively without creating shadow IT or security risks.

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 describe requirements in a way that lets Lovable generate a usable web application.
  • You create a landing page with a form and publish it on your own.
  • You build an internal tool with login, data storage and roles without any coding.
  • You develop clickable prototypes and use them to align with stakeholders.
  • 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

Module 1 From idea to first draft
  • What Lovable can do and what happens behind the scenes, explained plainly
  • Breaking an idea down into goal, user groups, pages and content
  • Writing your first prompt and judging the result
  • Making small, precise change requests instead of starting from scratch
Module 2 Landing pages and web presence
  • Campaign and event pages with a clear message and call to action
  • Adding images, copy and your corporate design
  • Contact and registration forms that store submissions
  • Checking how pages look on mobile and desktop
Module 3 Internal tools and forms
  • Building input forms, overview lists and simple dashboards
  • Storing data in a database without writing SQL
  • Setting up logins and roles: who may see and change what
  • Spotting typical errors and fixing them with the undo history
Module 4 Customer portals and prototypes
  • Simple portals for customers or partners with a protected area
  • Clickable prototypes for product ideas and stakeholder presentations
  • Collecting feedback and implementing iterations in a focused way
  • Handing a well-documented prototype over to development or IT
Module 5 Publishing, security and ground rules
  • Publishing your app and making it available on your own domain
  • Personal data, GDPR and coordination with IT and data protection
  • Costs, usage limits and realistic effort
  • Recognising when a project belongs in professional developers’ hands
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

Confident use of a web browser and standard office software is all you need. No programming knowledge is required. Experience of academic work in a discipline; programming skills are helpful but not required.

Technical requirements

A laptop with an up-to-date browser, a Lovable account (a paid plan is recommended for intensive use) and free GitHub and Supabase accounts.

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.

  • Mon, 23 November 2026 Online Request
  • Tue, 1 December 2026 Hanover Request
  • Mon, 14 December 2026 Online Request
  • Fri, 8 January 2027 Online Request
  • Thu, 21 January 2027 Munich Request
  • Wed, 27 January 2027 Online Request

Frequently asked questions

Do I own the code Lovable generates?

Yes. The output is standard React and TypeScript code that you can push to your own repository via the GitHub sync. From there it can be developed, tested and deployed entirely independently of Lovable. In the course we show you how to set this up properly from day one so you avoid any lock-in.

Is Lovable suitable for production applications?

For prototypes, MVPs, internal tools and modest customer-facing apps, yes, provided you review security and the data model carefully. Supabase Row Level Security, secrets handling and the maintainability of a growing codebase all need attention. The course covers exactly which checks make sense before you go live.

How does Lovable differ from Cursor or Claude Code?

Lovable is a browser-based app builder with a fixed stack, built-in hosting and Supabase integration, which makes it ideal for getting started quickly. Cursor and Claude Code work inside any existing codebase and technology. Many teams combine them: start in Lovable, then continue in the editor via GitHub.

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 “Lovable for Researchers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 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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