AI training and digital transformation with outcomes that are measurable.

Gives each member of staff their own way of working with AI: a personal prompt set, a routine for everyday tasks, and the ability to produce documents, presentations, images and spreadsheets that are usable as they are.

1/7Target learners

• a)Civil servants and public employees of provincial departments, agencies and public service units
• b)Office staff and professional specialists of enterprises
• c)Division-level managers who need to organize AI adoption in their unit

2/7Competency objectives

1. 01Use an AI assistant across the full range of daily individual work: drafting, summarizing, analysis, presentation and data handling
2. 02Produce deliverables in several formats — documents, presentations, images and spreadsheets — at a standard that is usable as it is
3. 03Build a personal prompt set for one's own work and standardize it so colleagues can reuse it
4. 04Recognize the data, security and legal risks of using AI, and apply safe-use rules
5. 05Verify AI-generated output before putting it to use

3/7Level-based pathway

1. Level 1 · Foundation

   4 sessions × 3 hours

   Foundations of working with AI

   Understand how generative AI works, write correctly structured prompts and use AI safely with the organization's data.

2. Level 2 · Application

   5 sessions × 3 hours

   Applying AI to a business process

   Choose a real process of the organization, produce a multi-format document set that is usable as it is, and measure the change in processing time.

4/7Duration

9 sessions · 27 hours

Duration by level for Personal AI Training

Level 1 — Foundations of working with AI	4 sessions × 3 hours
Level 2 — Applying AI to a business process	5 sessions × 3 hours

5/7Delivery formats

• On-site at the organization
• Live online with an instructor
• Blended

Class size

15–30 learners per class

Prerequisites

• Learners have AI tool accounts provided by the organization or agreed before the course
• The organization designates one business process as the practical case for level 2

6/7Mandatory deliverables

1. L1A personal set of 10 prompts for real work tasks, with notes on purpose and results
2. L2A standardized prompt set for one business process of the organization (≥ 5 steps), with a user guide
3. L2A four-format document set for that process: document, presentation, image and spreadsheet
4. L2An application report comparing processing time before and after adoption

7/7Pass criteria

• Full attendancePresent at ≥ 80% of sessions, with attendance records
• All outcomes of the registered levels achievedEach outcome meets its published threshold, with evidence kept on file
• Mandatory deliverables acceptedAcceptance record signed by the instructor and the organization's representative

Outcome ledger — CT-01 · 6 rows

Code	Outcome	Evidence	Pass threshold
CĐR-1.1	Write prompts that contain all four components — context, request, format, criteria — for document tasks	Practical exercise on 5 tasks, scored against a rubric	Pass ≥ 4 of 5 tasks
CĐR-1.2	Classify information that may and may not be entered into AI tools under the organization's rules	20-question scenario test	≥ 80% correct answers
CĐR-1.3	Verify AI-generated output and point out the errors in it	Review of 3 documents with planted errors	Detect ≥ 2 of 3 errors
CĐR-2.1	Build a standardized prompt set for a real business process that others can reuse	Group deliverable, appraised by the instructor and the organization's representative	≥ 70/100 rubric points
CĐR-2.2	Measure and report the change in processing time of the process after adopting AI	Application report with before-and-after measurements	Real measurements on ≥ 1 process
CĐR-2.3	Produce deliverables in four formats — document, presentation, image and spreadsheet — usable as they are for one process	The submitted document set, scored against a rubric by the instructor and the organization's representative	≥ 3 of 4 deliverables reach the usable-as-is standard

Trains technical teams and specialists with a programming background to use AI coding assistants to build, test and hand over internal software.

1/7Target learners

• a)Developers and IT specialists of enterprises and public service units
• b)Technical staff in charge of information systems at departments and agencies
• c)Professional specialists with basic programming knowledge who need to build their own data-processing tools

2/7Competency objectives

1. 01Use an AI coding assistant across the whole development lifecycle: design, coding, testing, documentation
2. 02Build a complete internal application from a real business requirement of the organization
3. 03Set up code review and testing procedures to control the quality of AI-generated code
4. 04Package, deploy and hand over the product with operating documentation

3/7Level-based pathway

1. Level 1 · Foundation

   5 sessions × 3 hours

   Foundations of coding with AI

   Set up the environment, describe requirements to AI and control AI-generated code with automated tests.

2. Level 2 · Application

   6 sessions × 3 hours

   Building an internal application

   Develop, in short iterations, an application that solves a real problem of the organization, with security review and documentation.

3. Level 3 · Mastery

   3 sessions × 3 hours

   Deployment and handover

   Put the product on the organization's environment, run it in trial operation and hand it over so that others can take it on.

4/7Duration

14 sessions · 42 hours

Duration by level for AI Code Training

Level 1 — Foundations of coding with AI	5 sessions × 3 hours
Level 2 — Building an internal application	6 sessions × 3 hours
Level 3 — Deployment and handover	3 sessions × 3 hours

5/7Delivery formats

• On-site at the organization
• Live online with an instructor
• Blended

Class size

8–16 learners per class

Prerequisites

• Learners can already program at a basic level in at least one language
• The organization provides the business problem and a test deployment environment for levels 2–3

6/7Mandatory deliverables

1. L1A command-line tool or data-processing script that solves a real task, with a test suite
2. L2A complete internal application solving a real problem of the organization, with source code in a version control system
3. L2Design documentation and a user guide
4. L3The product deployed on the organization's environment, with operating documentation and a handover record

7/7Pass criteria

• Full attendancePresent at ≥ 80% of sessions, with attendance records
• All outcomes of the registered levels achievedEach outcome meets its published threshold, with evidence kept on file
• The software product accepted in real operationFunctional acceptance record and trial operation at the organization

Outcome ledger — CT-02 · 6 rows

Code	Outcome	Evidence	Pass threshold
CĐR-1.1	Turn a business requirement into a technical specification that the AI assistant implements correctly	Practical exercise on 3 requirements, scored on execution results	≥ 2 of 3 requirements run correctly
CĐR-1.2	Write an automated test suite covering the main cases of a function	Test suite delivered with the level 1 deliverable	Covers ≥ 70% of the main processing branches
CĐR-2.1	Build a complete, working application that meets the agreed business requirements	Functional acceptance against the requirements list	≥ 90% of mandatory requirements met
CĐR-2.2	Detect and fix common security flaws in AI-generated code	Code review exercise with planted flaws	Detect ≥ 4 of 5 flaws
CĐR-3.1	Deploy the product to the real environment and operate it stably through the trial period	Trial operation record	Stable for ≥ 5 working days with no critical failure
CĐR-3.2	Hand over the product with documentation so that others can operate it without the author	Operation check by someone who did not take part in development	≥ 90% of documented operations performed correctly

Advisory and delivery of digital transformation in stages, each tied to its own handover deliverable and acceptance criteria.

1/7Participants

• a)Leadership and the digital transformation unit of enterprises
• b)Departments, agencies and district- and commune-level People's Committees implementing digital transformation under the province's plan
• c)Public service units that need to digitize their service delivery processes

2/7Competency objectives

1. 01The organization has a quantified picture of its current state and a digital transformation roadmap by maturity level
2. 02At least one core process is digitized, operated in practice and measured
3. 03The internal team is able to operate, maintain and extend the results after handover
4. 04A complete project file for appraisal, acceptance and reporting

3/7Level-based pathway

1. Level 1 · Foundation

   4 weeks

   Current-state assessment and roadmap

   Survey processes, data and infrastructure; score digital maturity and build a costed roadmap.

2. Level 2 · Application

   8 weeks

   Pilot delivery

   Redesign and digitize the selected process, put it into operation with real users and measure the results.

3. Level 3 · Mastery

   4 weeks

   Operation and handover

   Standardize operations, train the team taking over and hand over the complete project file.

4/7Duration

16 weeks

Duration by level for Digital Transformation Services

Level 1 — Current-state assessment and roadmap	4 weeks
Level 2 — Pilot delivery	8 weeks
Level 3 — Operation and handover	4 weeks

5/7Delivery formats

• On-site at the organization
• Blended

Class size

Scope set by survey

Prerequisites

• The organization appoints a contact person with the authority to decide on the process selected for the pilot
• The organization provides access to the data and infrastructure needed for the survey

6/7Mandatory deliverables

1. L1Current-state assessment report
2. L1Digital transformation roadmap with a prioritized list of items and a budget estimate
3. L2A digitized process in real operation with real users
4. L2Pilot results report with before-and-after measurements
5. L3Operating documentation and usage rules
6. L3Handover record and complete project file for acceptance

7/7Pass criteria

• Conditional stage transitionThe previous stage's handover deliverable accepted in writing
• All outcomes of each stage achievedEach outcome meets its published threshold, with evidence kept on file
• Complete project fileReconciled against the file checklist agreed in stage 1

Outcome ledger — CT-03 · 6 rows

Code	Outcome	Evidence	Pass threshold
CĐR-1.1	The organization's current state is described by a set of indicators comparable over time	Current-state assessment report	≥ 90% of core processes surveyed and scored
CĐR-1.2	The roadmap is approved by the organization's leadership with clear items, sequence and budget	Written approval of the roadmap	Approved in writing
CĐR-2.1	The pilot process is operated in practice by the organization's users	Operation log and confirmation record	≥ 80% of intended users use it regularly over 4 weeks
CĐR-2.2	Pilot results are measured with the indicators agreed in stage 1	Pilot results report	Before-and-after data on all selected indicators
CĐR-3.1	The internal team operates the system on its own after handover	Independent operation check	≥ 90% of standard operating scenarios handled correctly
CĐR-3.2	The project file is complete under the organization's acceptance requirements	Reconciled file checklist	100% of mandatory file items present

Equips the organization to keep control of its data once AI tools enter daily work: classify the data, issue usage rules, monitor use, and handle a leak when one occurs.

1/7Target learners

• a)IT and information-security staff of agencies and organizations
• b)Division and department heads responsible for issuing internal rules on AI use
• c)Staff who routinely handle the personal data of citizens or customers

2/7Competency objectives

1. 01Classify the organization's data by sensitivity and determine which data must never enter an AI tool
2. 02Issue safe-use rules for AI and a list of approved tools
3. 03Set up monitoring of AI use and detection of signs of data leakage
4. 04Handle a data-leak incident according to a rehearsed procedure

3/7Level-based pathway

1. Level 1 · Foundation

   4 sessions × 3 hours

   Recognizing data risk in AI use

   Understand where data goes once it enters an AI tool, classify the organization's data, and recognize the common leakage paths.

2. Level 2 · Application

   5 sessions × 3 hours

   Setting rules and controls

   Turn the classification into AI usage rules that take effect, with a list of approved tools and a safe configuration for each.

3. Level 3 · Mastery

   3 sessions × 3 hours

   Monitoring and incident response

   Put the policy into operation: track usage, detect anomalies, and rehearse the handling of a data-leak incident.

4/7Duration

12 sessions · 36 hours

Duration by level for AI Data Security Training

Level 1 — Recognizing data risk in AI use	4 sessions × 3 hours
Level 2 — Setting rules and controls	5 sessions × 3 hours
Level 3 — Monitoring and incident response	3 sessions × 3 hours

5/7Delivery formats

• On-site at the organization
• Live online with an instructor
• Blended

Class size

10–20 learners per class

Prerequisites

• The organization provides its data inventory and the list of AI tools actually in use
• The organization appoints a contact person with the authority to submit the policy for signature after the course

6/7Mandatory deliverables

1. L1A data classification table for the organization, marking the categories that must never enter an AI tool
2. L2A draft AI usage policy for the organization, ready to be submitted for signature
3. L2A list of approved AI tools, with a safe configuration for each
4. L3A data-leak response procedure, with roles and time limits
5. L3A record of one rehearsal handling a simulated incident

7/7Pass criteria

• Full attendancePresent at ≥ 80% of sessions, with attendance records
• All outcomes of the registered levels achievedEach outcome meets its published threshold, with evidence kept on file
• The policy and procedure are taken up by the organizationAcceptance record with the organization's leadership confirming receipt of the draft policy

Outcome ledger — CT-04 · 6 rows

Code	Outcome	Evidence	Pass threshold
CĐR-1.1	Classify the organization's data correctly by sensitivity and identify the categories barred from AI tools	The submitted classification table, reconciled against the organization's real data inventory	≥ 90% of the data items checked classified correctly
CĐR-1.2	Recognize data leakage paths in everyday AI use scenarios	Scenario test of 25 questions	≥ 80% of answers correct
CĐR-2.1	Draft AI usage rules clear enough for staff to follow without further explanation	The draft policy, appraised by the instructor and the organization's representative against a rubric	≥ 70/100 rubric points
CĐR-2.2	Anonymize a real case file to the point where entering it into an AI tool no longer identifies any individual	Anonymization exercise on a sample file, cross-reviewed	No identifying field left anywhere in the checked sample
CĐR-3.1	Handle a simulated data-leak incident according to the procedure and within the time limit set	Rehearsal record with each step timed	All mandatory steps completed in ≤ 60 minutes
CĐR-3.2	Hand over the policy and procedure so the organization runs them without the instructor	Operational check by a staff member who did not attend the course	≥ 90% of the procedure's steps performed correctly