Top 10 AI Prompts and Use Cases and in the Education Industry in Ukraine

Ukraine's education system is moving from promise to practice as research and recent initiatives show AI's real potential: a Futurity Education study maps a three‑tier deployment - building AI infrastructure, optimising learning with personalised tools, and automating support processes - to deliver individualised learning, smarter communication, and safer automation (Futurity Education study on AI deployment in Ukrainian education); at the same time, a May 2025 Ministry of Education and Science partnership with Panopto is bringing generative video tools like Elai (over 80 AI voice avatars and 75+ languages) to universities so teachers can scale multilingual, interactive lessons (Ministry of Education and Science of Ukraine partnership with Panopto on Elai generative video tools).

Practical webinars and local projects underline one clear “so what?”: when ethical guardrails and human oversight pair with hands‑on prompts and tools, AI can turn scarce resources into personalised pathways for millions of Ukrainian learners (Streamit webinar on transforming learning in Ukraine with AI).

“This initiative reflects our commitment to equipping educators with innovative tools that enhance teaching and learning, even in the face of disruption.”

The methodological backbone for this Ukraine‑focused analysis combines documentary content analysis and SWOT work from Futurity Education with expert‑driven ratings, surveys, and small‑scale pilots.

“The Role of Artificial Intelligence in Improving the Quality of Education and Research”

The study used content analysis and SWOT to surface both AI's adaptability and the security risks tied to digitalisation (Futurity Education study: The role of AI in education and research - content analysis and SWOT); a complementary Futurity review compiled expert organisations, scored AI solutions and modelled a practical three‑tier deployment (infrastructure → optimisation → automation) to prioritise tools for Ukraine (Futurity Education review: Application of AI in Ukrainian education - expert scoring and three‑tier deployment); and conference proceedings on VR used mixed‑methods - experimental lessons, surveys and interviews - to validate immersive pilots that boost engagement and 21st‑century competencies (Conference proceedings: VR for competence development in education - mixed‑methods validation).

Triangulating literature, expert ratings, and classroom pilots - while keeping ethical and digital‑literacy gaps in view - shapes the practical prompts and use cases that follow; one memorable finding: SWOT analysis repeatedly flags security as a red line for deployment in a nation facing hybrid threats.

Adaptive curricula in Ukraine are already moving from concept to classroom-ready practice: simple, teacher‑led prompts let AI assemble personalised learning paths for hundreds of students at once while preserving oversight and integrity - for example, a practical “Socratic tutor” prompt (ask ChatGPT to explain a concept in three bite‑sized steps, then generate five scaffolded questions and a level‑appropriate glossary) can kick off a cycle of draft→AI feedback→revision that teachers verify with a 2‑minute oral check and an “AI use note” on submissions; these workflows, pulled from practical guidance for Ukrainian schools, scale individual support for millions without replacing teaching and can even reach learners in bomb shelters or evacuation zones via cloud lessons (AI and ChatGPT in Ukraine's schools - personalized learning paths | Complete AI Training).

Pairing this with national digital platforms and IT Studios ensures materials are accessible and resilient across regions (FIAP/EU4DigitalUA report: Empowering Ukraine through digital learning), while clear policies - published AI‑use rules, redesigning assessments to include process portfolios, and short teacher workshops - provide the guardrails that make personalization both powerful and safe.

“Any technology contributes to the development of systems, including education. The question is how we will use it,” he said.

Intelligent Tutoring Systems (ITS) can act as a quiet, tireless teaching assistant for Ukrainian classrooms by delivering real‑time, adaptive feedback that helps students correct course before misconceptions harden; recent open‑access research describes ITS that personalise STEM instruction and feedback in the moment (Adaptive intelligent tutoring systems for STEM education - real‑time personalised, adaptive learning), while practitioner summaries explain how ITS architectures (domain, student, tutoring and interface models) power immediate, targeted instruction and scalable data insights (AI in Education: the rise of intelligent tutoring systems - immediate feedback and targeted instruction).

For Ukraine this means affordable, classroom‑aligned ITS pilots can extend teachers' reach - automating routine hints and formative checks so instructors focus on higher‑value interventions - and support national STEM goals identified in systematic reviews of ITS effectiveness in STEM domains (Systematic review: ITS effectiveness in STEM education).

A simple, classroom-ready teacher prompt might read:

Diagnose this student's solution, generate three scaffolded hints ordered from minimal to explicit, and suggest a two‑question formative check for misconceptions.

That blend - adaptive algorithms, clear pedagogical scaffolds, and teacher oversight - turns ITS from a novelty into a practical tool for resilient, personalised learning across Ukraine, from urban labs to regional schools.

Automated assessment and grading can be a game‑changer for Ukraine's classrooms - speeding up feedback, producing consistent scores, and converting a pile of 200 essays or problem sets into question‑level analytics in minutes - yet it works best as a calibrated partner, not a replacement for teachers.

Objective formats and programming tasks map neatly to auto‑grading pipelines (unit tests, static analysis and rubric checks), while AI‑assisted scoring can offer first‑pass feedback on essays; both approaches free instructors to focus on higher‑order review and remediation if human oversight is built in and results are regularly audited (Ohio State study on AI and auto-grading capabilities, ethics, and educator roles).

Practical rollouts should start with pilots, clear disclosure to students, and safeguards against bias - remember, AI is powerful but

not a magic wand

and can introduce fairness and transparency risks that require policy and training (MIT Sloan analysis of AI-assisted grading fairness and ethical risks).

When paired with reliable connectivity, teacher validation routines, and iterative audits, automated grading can scale assessment in Ukraine while preserving pedagogical judgement and integrity (Automated grading use case: detailed analytics and time savings).

AI‑powered virtual assistants and chatbots are becoming practical helpers across Ukrainian education: academic research flags chatbots as "highly relevant" for interactive Ukrainian language learning (AI-based chatbots for Ukrainian language learning study (Zenodo)), while surveys of Ukrainian university students find widespread, satisfied use of ChatGPT for information searches and language tasks - editing, polishing and drafting course work (Using ChatGPT as a learning tool study (SSRN)).

Homegrown tools bring these capabilities to life: the Interlocutor app offers conversational practice, instant grammar correction, spoken/text dialogues and exam prep (there's even a long free access period), with realistic interactions "so realistic that you might mistake him for a real person" (Interlocutor AI app overview and features (Dev.ua)).

Complementary initiatives translate and dub high‑quality course materials into Ukrainian, preserving technical accuracy and speaker tone to widen access to world‑class content.

Put together, chatbots and assistants can deliver scalable language practice, quick formative feedback, and translated learning resources that reach students and relieve routine burdens on teachers - provided human review and quality checks remain in place.

“At my school, I saw firsthand how language barriers prevented many Ukrainian students from accessing world-class education,” says Lipkevych.

Smart content generation is making curriculum design practical for Ukraine's classrooms by turning time‑consuming planning into editable, standards‑aligned drafts: platforms like SEQTA AI Assist lesson drafting tool promise curriculum‑linked lesson plans in seconds while preserving leader visibility and blocking PII, Google's Gemini for Education AI tools offers free, admin‑managed AI tools to re‑level texts, generate assessments and build “Gems” for localized expertise, and free generators such as Heuristica AI Lesson Plan Generator (Ukrainian) can produce lesson materials directly in Ukrainian so teachers can localize content fast.

These tools support differentiation, rubric creation and quick standards alignment (useful for world‑language CEFR mappings), so a teacher's two‑hour planning slog can become a polished, classroom‑ready draft in moments - while human review, privacy safeguards and national AI literacy courses ensure materials stay accurate and resilient across regions.

“We also included essential modules on AI regulation in the EU, as Ukraine moves toward alignment. Another focus is using AI-generated content ...”

Immersive XR - combining VR, AR and 360° simulations - is already a practical lever for Ukraine's vocational and language education: platforms like ClassVR virtual and augmented reality for language learning show how virtual field trips and themed scenes (Spanish tapas bars, Italian cafés) help students practise vocabulary and real‑world interactions, while Apple Education Vision Pro immersive language practice examples put learners into richly detailed scenarios - from ordering coffee to a simulated job‑fair interview - where AI companions or pre‑recorded native speakers provide patient, repeatable practice and scaffolded feedback.

For vocational tracks, XR offers low‑risk skills rehearsal and simulated soft‑skills assessments that speed time‑to‑competence; policymakers and school leaders can prioritise access and content development (including web/tablet alternatives to expensive headsets) to scale impact without breaking budgets (ITIF report on the promise and potential of immersive learning).

The payoff is memorable: learners who can “try before they do” in a safe virtual space return to the real world more confident, fluent and job‑ready.

“the winners in the upcoming race to adapt will be those able to leverage the full range of opportunities it offers for language learning.”

Learning analytics and early‑warning systems give Ukrainian schools a practical way to catch trouble before it becomes a crisis: machine‑learning models trained on past dropout cases can scan grades, attendance, LMS activity and even softer signals (discussion posts, counselling notes or changed routines) to flag students showing early warning signs, enabling fast, targeted supports rather than belated remediation - an approach shown to cut dropouts in practice (Open Data Science case study: AI Helps Reduce School Dropouts).

When paired with big‑data workflows that integrate academic and non‑academic factors, institutions can personalise interventions, optimise scarce resources and focus advising where it matters most (Geniusee article: Big Data in Education - analytics and personalised learning).

Real‑world examples and best practices - like systems that monitor hundreds of indicators or even campus activity to predict risk - show measurable retention gains and point to practical steps for Ukraine: start with narrow pilots, protect student privacy and cyber‑security, audit models for fairness, and use simple dashboards so teachers can act on alerts before grades fall (eSelf.ai guide: Predictive Analytics in Education - use cases and best practices).

Spotting a drop in library visits or a sudden fall in forum posts can be the tiny signal that keeps a student in school.

Teacher support in Ukraine will hinge less on cutting‑edge hype and more on practical automation that gives educators back time for students: workflow tools can automate attendance, reporting, parent communications and routine grading so a teacher reclaims nearly six hours a week - roughly six extra weeks across a school year - to spend on instruction and mentoring (see the SchoolAI findings on hours saved SchoolAI report: AI lightens educator workload).

No‑code platforms and AI copilots make those gains reachable without big IT projects, enabling schools to digitise approvals, generate documents and route tasks fast while keeping data governance in place (FlowForma analysis: automation in education workflows).

Equally important is professional development: short, focused PD on prompt design, pilot metrics and privacy safeguards (FERPA/GDPR style controls) plus six‑week classroom pilots let Ukrainian leaders measure hours saved, monitor bias, and scale what actually improves wellbeing and retention (EdTech Magazine guide: AI for teachers - defeating burnout and boosting productivity).

The policy “so what?” is vivid and simple: when automation is paired with teacher training and clear guardrails, it turns administrative overload into classroom time that actually changes learning outcomes.

“As a society, we underestimate the effort it now takes to be a teacher.”

Keeping Ukrainian classrooms honest - and resilient - means turning AI ethics into concrete student modules that teach not just “don't cheat,” but how to document, disclose and interrogate AI use: the AI Literacy Framework for Academic Integrity and Citation lays out core competencies like vigilance, transparent citation and practical templates (chat consolidation, shareable links and simple AI‑disclosure forms) that can be built straight into assignments.

Research also shows educators find alignment with AI ethics complex and situational, so training must combine theory with classroom routines - modeling documentation in live demos, offering exemplars, and using process portfolios as assessment artifacts to make authorship visible (Research: Educators' AI Ethics Alignment and Classroom Practices).

For Ukrainian institutions, that means pairing student modules with teacher scaffolds and new institutional roles - assessment design and validation, for example - to audit automated scoring and keep stakes fair (Assessment Design and Validation to Audit Automated Scoring).

The memorable payoff: requiring a single shareable chat link or annotated draft can turn a “perfect” essay from a red flag into a transparent learning trace, protecting integrity while teaching a practical digital literacy that will matter long after any one tool changes.

Policy roadmaps for Ukraine's education and government systems should foreground Retrieval‑Augmented Generation (RAG) as a pragmatic, legally cleaner building block for macro planning: RAG can turn static laws, guidance and datasets into live, queryable knowledge bases that power scenario modelling, policy trade‑offs and real‑time briefings so a minister or school director can see sourced, up‑to‑date implications of a change in minutes rather than weeks.

Design work should stitch together technical components - embeddings, retrievers and generators - while embedding governance gates, role‑based access and audit logs so outputs remain explainable and auditable (see practical architectures in the Datategy RAG guide and the Gov‑RAG framework for e‑government).

Legal and procurement teams can gain confidence because RAG leaves base models unchanged while surfacing only authorised documents at runtime, reducing fine‑tuning legal risk and making compliance more tractable (as argued in JustSecurity).

The roadmap should pair narrow pilots, clear evaluation metrics and enterprise data readiness with leadership, partnerships and policy‑level governance so iterative pilots scale into robust, explainable systems that support resilient decision‑making across Ukraine's ministries and universities.

“RAG is the ability to say you know what, I actually know the information I want to have a relationship with,” Bonnell said.

Start small, keep humans in the loop, and scale what clearly works: Ukrainian schools should adopt the three‑tier deployment Futurity Education recommends - build resilient AI infrastructure, roll out optimisation tools for personalised learning, and automate low‑risk processes - while using the Ministry's practical classroom guidelines to protect students and data (Futurity Education three‑tier AI deployment recommendation, Ukrainian Ministry practical AI classroom guidelines (UNN)).

Prioritise short teacher PD sprints, transparent AI‑use disclosure in assignments, and narrow pilots that measure learning gains and fairness; after all, a teacher who knows how to use artificial intelligence effectively is less fatigued, has more time for creativity, and can provide better support for students.

Leverage partnerships and platforms - from IT Studios and Panopto's Elai pilots to targeted bootcamps - to build capacity quickly; for example, Nucamp's 15‑week AI Essentials for Work pathway offers a practical, prompt‑focused route for educators and staff to gain workplace AI skills (Nucamp AI Essentials for Work 15‑week bootcamp registration).

The immediate “so what?”: measured pilots, clear guardrails, and focused PD turn fragile access into equitable, resilient learning that reaches classrooms, shelters, and displaced learners across Ukraine.

“This initiative reflects our commitment to equipping educators with innovative tools that enhance teaching and learning, even in the face of disruption.”