Top 10 AI Prompts and Use Cases and in the Education Industry in Czech Republic

The Czech education system is poised for rapid change as policymakers and universities push to embed AI skills across curricula: the national AI strategy calls for reforms in primary, secondary and higher education, while a recent study assessing AI literacy in teacher education highlights gaps in teachers' practical skills and ethical readiness (AI literacy study - University of Ostrava).

Complementary guidance from a Charles University working group offers practical recommendations for safe classroom use of generative tools (Charles University AI recommendations for educators and students), yet public uptake remains uneven - only about 38% of Czechs report occasional hands‑on use of generative AI and analysts warn that over 2.3 million Czech jobs could be affected in the next decade, underscoring urgent upskilling needs (CEDMO Trends survey on generative AI use in the Czech Republic).

Practical, short programs - for example Nucamp's 15‑week AI Essentials for Work - can help teachers and administrators turn strategy into classroom-ready skills and safer, more adaptive learning experiences.

Program	Length	Cost (early bird)	Registration
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work - Nucamp Registration | AI Essentials for Work - Nucamp Syllabus

To choose the Top 10 prompts and use cases for Czech classrooms, selection began with evidence: the University of Ostrava survey of teacher AI literacy - a questionnaire across educational levels that highlights gaps in practical skills, ethical awareness and classroom integration - anchored our priorities (University of Ostrava AI literacy teacher education study).

From there, criteria favoured teacher‑ready interventions that close those gaps (scaffolded lesson generation, formative feedback, multilingual localisation), match policy and implementation best practices from global guides, and scale affordably for Czech schools.

Practicality and ethics were weighted heavily: choices reflect TeachAI's policy ideas on building capacity, promoting AI literacy and providing clear guidance (TeachAI foundational policy ideas for AI in education), while prompt design priorities drew on recent coverage that frames prompt engineering as a classroom skill - the ability to turn standards into scaffolded, differentiated lessons in seconds (Prompt engineering guidance from eSchool News).

The result: use cases that are classroom‑immediately useful, ethically framed, and targeted to the concrete training needs Czech teachers reported.

“Can students use ChatGPT for assignments?”

Personalized adaptive tutors bring that long‑promised one‑to‑one help into Czech classrooms and homework by using mathematical student models and machine‑learning to tailor practice to each learner's gaps - examples from Masaryk University Adaptive Learning Research Group include Umíme česky, RoboMission and Practice Anatomy, projects whose results

“often have an immediate impact on thousands of users” - Masaryk University Adaptive Learning Research Group research page

In practice this means students get bite‑sized, scaffolded exercises that follow their mastery curve while teachers receive analytics to target instruction, a design that helps boost retention and can reduce repeat‑course costs for providers (adaptive tutoring for Czech learners).

Emerging coverage shows these systems are reshaping tutoring by reimagining interaction and feedback, but the Czech rollout must pair technical gains with careful evaluation and bias detection to protect fairness and learning quality (eSchool News: How AI tutoring personalizes learning for students).

Automated grading is moving beyond simple rubric matching toward actionable, multilingual feedback that helps Czech teachers scale essay assessment without losing pedagogical nuance: an ERIC study describes and evaluates a multilingual automated essay scoring (AES) system that grades essays in three languages (ERIC study on multilingual automated essay scoring (Journal of Educational Measurement)), while recent research demonstrates a practical, cost‑effective route to both scoring and revising student writing using open‑source large language models (IEEE 2024 paper: automated essay scoring and revising with open‑source large language models), offering time‑efficient, content‑safe suggestions that can act like a tireless second reader proposing revision paths and draft improvements.

Czech‑specific tools are already emerging too: EVALD is cited as a pioneer AES application for Czech that evaluates texts from both native and non‑native speakers (EVALD: automated essay scoring application for the Czech language), pointing to immediate classroom uses for formative feedback, faster turnaround on drafts, and targeted teacher interventions when coupled with local validation and oversight.

For Czech students and academic staff, AI as a research assistant can turn the growing avalanche of papers from a paralysis point into a manageable workflow: a curated index of over 200 LLM research papers from 2025 offers a reliable pulse on reasoning, efficient training and multimodal advances (2025 LLM research papers roundup), while a focused SSRN analysis argues that LLM‑based tools can automate literature synthesis and free time for hands‑on fieldwork and theory development

Literature Reviews with LLM‑Based Tools - SSRN analysis

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In practice this means students and researchers can move from being buried in PDFs to producing draft syntheses, annotated bibliographies, and research questions faster - so supervisors see action instead of delay - provided local teams pair these tools with careful validation and digital curation.

Library staff and information professionals in Czech institutions should therefore lean into metadata tagging and information‑literacy training to make AI‑assisted search reliable and pedagogically useful (library assistants and digital curation), creating a workflow where faster synthesis leads to more time for experiments, supervision and classroom mentoring.

A Czech question‑bank and assessment designer built around Bloom's Taxonomy turns a pile of short quizzes into a learning ladder: start with clear, measurable verbs for Remember and Understand and then scaffold up through Apply, Analyze, Evaluate and Create so assessments map directly to classroom goals - think of the taxonomy as a six‑layer “cake” that helps teachers balance recall with real problem solving (Bloom's Taxonomy cake‑style hierarchy and verb examples).

Practical tools for item writers and test blueprints make it straightforward to distribute items across cognitive levels and avoid exams that reward only memorization; this improves validity and points teachers to where remediation or enrichment is needed (test‑blueprint guidance for balanced assessments).

For busy Czech faculties, ready‑to‑use question stems and higher‑order prompts speed item creation and classroom polling while preserving rigor - imagine swapping a stack of MCQs for a set of scenario vignettes that reveal clinical reasoning or civic judgment in seconds (100+ Bloom's question stems and higher‑order prompts).

When combined with careful rubric design, this workflow yields fairer, more diagnostic exams and a question bank that actually teaches, not just tests.

Bloom Level	Key Verbs / Examples
Remember	list, name, identify
Understand	describe, summarize, explain
Apply	demonstrate, solve, use
Analyze	differentiate, compare, diagram
Evaluate	judge, critique, defend
Create	design, generate, construct

Localising lessons from Czech into English, German and Slovak is not just translation work - it's an accessibility strategy that helps classrooms tap into linguistic assets and support migrant families with concrete, classroom‑ready resources; a handy round‑up for parents and schools points to tutoring, interpreters and free integration supports that ease children's transition (integration resources for multilingual families in Czech schools).

Good localisation plans mirror local search terms, UI labels and cultural examples rather than literal translations, so lesson pages, metadata and activity prompts actually reach the students and parents who need them - a practice SEO consultants recommend when adapting content for the Czech market (Czech localization and SEO best practices).

The effort aligns with EU guidance that treats multilingualism as a core competence for employability and social inclusion - note the EU finding that only about 42% of 15‑year‑olds reached B1/B2 in a first foreign language and far fewer in a second, a gap localisation and early language support can help narrow (EU guidance on multilingualism as a key competence).

The practical payoff is immediate: lessons that speak students' languages reduce friction, increase participation and make assessment and parental engagement work in more homes.

A student writing coach powered by AI can be a practical ally for Czech classrooms - offering sentence starters, on‑demand revision prompts and scaffolded feedback that turn frustrating blank pages into draftable paragraphs and free up the 6–8 hours many undergraduates spend wrestling with citation minutiae each term (a 2023 student survey found citation formatting alone can devour that time).

But practicality comes with guardrails: generative models are excellent at tutoring style and structure, as Khan Academy Writing Coach shows, yet scholars warn they do not reliably produce accurate, findable references (see the USC analysis “AI Cannot Cite Anything”), so every AI‑suggested source needs human verification.

A sensible Czech workflow pairs AI coaching with robust citation tools and process checks - use AI to rehearse and revise drafts, format verified entries with reference managers and guidebooks (see the 2025 citation and reference tools roundup), and run pre‑submission checks for similarity and proper attribution.

Course policies should require that students cite AI use, submit drafts or logs showing the writing process, and verify every reference; that hybrid approach preserves originality, teaches citation skills, and lets AI do the heavy lifting without outsourcing intellectual responsibility.

“More writing practice, less cheating - with AI on your team.” - Khan Academy Writing Coach

For Czech research labs, pairing an open lab notebook with a reproducible‑protocol generator turns messy, siloed notes into a durable research asset that speeds grants, publications and collaboration: adopt the simple discipline of dating and numbering pages, leaving a table of contents, and even labelling tubes with notebook page numbers so anyone can follow an experiment later - practical steps outlined in the Stemcell guide: How to Organize Your Lab Notebooks, References and Protocols (Stemcell guide).

Standardise with electronic templates for common procedures, paste printed instrument outputs into entries with a clear file path, and use reference managers like Mendeley or EndNote to avoid chasing citations; see the UCSF reproducible data management guide for organizing research data and the Stanford SPORR rigor and reproducibility resources and lab manual templates for ready blueprints for folder structures, version control and lab manual templates that Czech groups can adapt.

The payoff is tangible: cleaner handovers, faster writeups, and protocols that other teams can rerun reliably - a small change in record‑keeping that prevents weeks of confusion down the line.

An AI

administrative assistant

can make Czech grant hunting far less paper‑heavy by automating call scans, draft budgets, compliance checks and the mandatory publicity text that trips up many applicants: it can flag relevant streams like OP TAK's 1.5 billion CZK innovation window, map proposals to the wider OP TAC priorities, or prepare TWIST‑programme applications with the correct funding caps and timelines, then output ready‑to‑edit public‑acknowledgement language and logo placement to match TA ČR publicity rules.

This project is co‑financed from the state budget by the Technology Agency of the Czech Republic…

For schools, universities and edtech SMEs this means faster first drafts of budgets and workplans, auto‑checked eligibility (SME vs.

mid‑cap, TRL requirements), and press‑release boilerplate that meets funder templates - freeing staff to focus on pedagogy and partnerships rather than formatting.

Linkages to national recovery funds and digital skills measures also make it straightforward to build consortiums around RRP priorities (digital equipment and teacher upskilling) and to cite national R&D boosts when arguing societal impact; stitch these pieces together and a single prompt can produce a cohesive, funder‑ready package that otherwise takes weeks to compile.

Program	Key facts	Deadline / Timing	Max funding
OP TAK 1.5 billion CZK call for innovative IT projects	1.5 billion CZK for innovative IT projects; TRL≥5; ISKP21+ portal	Applications: 18 Feb – 30 May 2025 (may move if oversubscribed)	Allocated 1.5 billion CZK
OP TAC Operational Program Technologies & Applications for Competitiveness (2021–2027)	Operational Program Technologies & Applications for Competitiveness (2021–2027)	Programming period 2021–2027	CZK 81.5 billion (total program allocation)
TWIST applied AI research programme (Ministry of Industry and Trade)	Applied AI research & experimental development; intermediate bodies and partner rules	Contact Project Support Office by 24 Jan 2025; submission by 12 Feb 2025	Up to CZK 30 million (≤70% eligible costs)

Teacher professional development must move from optional workshops to a structured, role‑tailored pathway that closes the gaps the University of Ostrava identified in AI knowledge, practical classroom application and ethical awareness (University of Ostrava AI literacy study - Czech Republic).

Practical steps include short, certified modules for different roles - quick 2‑hour user briefings, deeper 2‑day practitioner courses and leadership fast tracks - that build both legal compliance under the EU AI Act and everyday skills like prompt design, bias checks and classroom scaffolding (see PwC's AI literacy guidance and courses).

Equally important are ready‑to‑use curricula and lesson packs: the AI for Children

AI Curriculum

supplies 43 resources, story‑driven lessons and webinars (with friendly characters Hoo and Ray for Grade 3+) that let teachers practice prompt engineering on real classroom activities rather than abstract theory (AI for Children AI Curriculum for primary and secondary schools - resources and lesson packs).

Anchoring professional development in national strategy goals - lifelong learning, reskilling and concrete classroom pilots - turns compliance into capability, so a teacher who starts a single two‑hour session can quickly convert a standard lesson into an AI‑guided, ethically framed activity that reaches more students and reduces prep time.

Immersive VR tools make 3D geometry feel less like a page‑bound abstraction and more like a workshop where students can step inside shapes, rotate polyhedra with their hands and test conjectures in real time - VR Math and other platforms let learners explore spatial concepts in three‑dimensional environments, turning a dry proof into a memorable, manipulable scene that boosts motivation and retention (Esade study: virtual and augmented reality in education).

In the Czech context this approach already fits national strengths in hands‑on technical education and industrial prototyping: early pilot projects show teachers and SMEs benefit from immersive content that bridges classroom theory and workplace practice, but roll‑out needs reliable broadband, teacher training and thought‑out lesson packs to succeed (Augmented reality and industrial metaverse in the Czech Republic - Xpert.Digital analysis).

For practical adoption, project‑based prompts can auto‑generate 3D lesson assets, step‑by‑step student tasks and assessment rubrics that make VR geometry scalable for schools - so a single headset becomes a portable studio for exploration, not just a flashy demo (VR geometry lesson plans for Czech teachers (2025 guide)).

The clear next steps for Czech educators and institutions are pragmatic and sequenced: use the National AI Strategy 2030 - Czech Ministry of Industry and Trade as a playbook to prioritise teacher training, curriculum updates and infrastructure while tapping targeted funding (the NAIS Action Plan outlines project investments of roughly CZK 19 billion and links to calls such as TWIST and OP TAK) - see the government summary of the strategy for guidance and timelines (National AI Strategy 2030 - Czech Ministry of Industry and Trade).

Parallel work is needed on governance: implement the EU AI Act rollout and national enforcement steps described in regulatory trackers so schools and universities have clear compliance and oversight pathways (EU AI Act implementation - global regulatory tracker (White & Case)).

Practically, start with small, funded pilots that pair short certified teacher modules with classroom trials, rigorous bias and impact checks, and industry‑school partnerships; a two‑hour upskilling session can be the pivot that converts a standard lesson into an ethically framed, AI‑guided activity and unlocks broader rollout.

Program	Length	Cost (early bird)	Registration
AI Essentials for Work	15 Weeks	$3,582	Enroll in Nucamp AI Essentials for Work (registration) | AI Essentials for Work syllabus - Nucamp

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