Online Generative AI Training for Thai Manufacturing: Live Exercises and Assessment

Practical Approaches to Designing “Online Generative AI Training” for Manufacturing Sites

In recent years, the integration of operations between multiple manufacturing sites in Thailand and headquarters in Japan has accelerated, making the enhancement of digital skills on the shop floor an urgent priority. The introduction of online generative AI training has gained attention as a means to share and standardize on-site knowledge and expertise in real time, regardless of physical distance or shift differences. For example, there is a growing movement to leverage generative AI to efficiently organize and utilize various documents generated daily in manufacturing environments—such as shift handover notes, discrepancies between SOPs (Standard Operating Procedures), and the documentation of quality nonconformance reports.

However, the design and implementation of synchronous online training require practical considerations that go beyond simply using tools. This article provides a concrete editorial proposal for the optimal design and evaluation points of online generative AI training, specifically tailored for manufacturing sites in Thailand.

Target Audience and Article Scope

This article is primarily intended for the following professionals working at manufacturing facilities in Thailand (including automotive parts, electronics, food processing, etc.):

• Factory managers and production control supervisors at local Thai plants
• On-site leaders in quality assurance (QA), maintenance, and sales management departments
• HR development and DX (Digital Transformation) leads at Japanese headquarters
• IT department and information security administrators

Please note that this article focuses exclusively on practical aspects of training design, operation, and evaluation on the shop floor, and does not cover price comparisons or specific vendor selection. For considerations on internal costs and ROI, please refer to “Cost Structure and Estimation for Generative AI Training”.

What Synchronous Online Training Can and Cannot Do

What It Can Do

• Simultaneous Participation Across Sites and Multinational Teams

Multiple factories in Thailand and Japanese headquarters can participate in the same training in real time. On-site issues and knowledge can be shared instantly, with Q&A sessions held on the spot.

• Practical Exercises Using On-site Data and Documents

It is easy to design exercises using real on-site materials—such as shift handover notes, different SOP versions, and nonconformance reports—by using sample data that excludes personal or confidential information.

• Bilingual (Japanese/Thai) Facilitation

By utilizing interpreters or automatic translation tools, the language gap between Japanese headquarters and Thai sites can be minimized.

Limitations and Points to Note

• On-site Equipment-Based Practical Training Not Possible

Physical training involving machinery operation or on-line activities cannot be replaced by synchronous online training.

• Impact of Network Issues and Device Malfunctions

Participant experience can be significantly affected by network environment or device performance. Pre-session connection checks and backup plans are essential.

• Maintaining Participant Focus is Challenging

Long lectures or one-way presentations can be burdensome for shop floor workers. Interactive design is crucial.

Reference: The Generative AI Governance Guideline by ETDA Thailand provides a summary of risks and security considerations when utilizing AI. When designing training, it is important to balance on-site realities with risk management.

Role-Based Examples: Designing Training Exercises Linked to On-site Operations

When designing online generative AI training, it is essential to set exercise themes that are directly connected to the actual operational challenges of each job function. Below are editorially recommended examples of exercise design by role (all data used are synthetic samples):

By designing exercises tailored to the actual work of each role, participants can acquire practical skills aimed at solving real operational issues, rather than just learning how to use AI tools. The NIST AI RMF Generative AI Profile (2024) emphasizes the risks of generative AI “hallucinations” (confabulation) and the importance of evaluating output reliability. It is highly recommended that training exercises always include procedures for verifying and reviewing AI outputs.

Preparation: Verifying Tools, Accounts, and Connection Environments

1. Selection and Approval of Tools

• Select AI Tools Compliant with Internal Policies

For example, under the official OpenAI business data protection policy, inputs and outputs from OpenAI Business, Enterprise and API services are not used to train models by default. Other providers and personal plans may have different terms, so check the specific product, contract and administrator controls before use.

• Risks of Using Free or Personal Accounts

Free or personal accounts carry a higher risk of data being provided to third parties or information leakage. As a rule, use business accounts for operations.

2. Participant IDs, Devices, and Connection Environment

• Personal Authentication and Access Management

Centralized management of training accounts, access log retrieval, and strict password policies.

• Device and Browser Compatibility

Verify operation on major browsers (Chrome, Edge, Safari, etc.). Support for smartphone participation should be limited.

• Network Stability

Test factory Wi-Fi or VPN connections in advance. Inform participants of minimum bandwidth requirements.

3. Bilingual Facilitation and Support System

• Design for Simultaneous Japanese/Thai Facilitation

Arrange interpreters or use automatic translation tools (e.g., Zoom simultaneous interpretation, real-time subtitles). Share site-specific terminology and proper nouns in advance.

• Establish a Support Desk

Set up a system to respond quickly to technical issues or questions during training.

Focus on “On-site Fit” in Training Design, Not Just Price or Vendor Comparison

When considering the introduction of online generative AI training, focusing solely on price comparisons or vendor selection can lead to mismatches with on-site operations and operational risks. In particular, the flexibility of training design and its alignment with on-site challenges are directly linked to long-term skill retention and operational improvement. For more on internal cost structures and ROI, please see this article.

Editorial Recommendation: Practical Approaches to Training Design

To achieve results on the shop floor, the design of online generative AI training should be customized to the actual operations of each factory or department, rather than relying on a one-size-fits-all “magic metric” or fixed score. For example, the OpenAI Academy’s practical courses emphasize hands-on exercises and output review processes closely linked to real operational challenges—an approach that can be applied to factory training as well.

The UNESCO AI Competency Framework for Teachers offers an educational analogy for designing staged competencies. Separately, ISO/IEC 42001 concerns organizational AI management and continual improvement. Neither prescribes a manufacturing training curriculum or completion standard. Drawing on these ideas, our editorial proposal is a cycle of exercise task → AI output review → feedback → repeat exercise.

Separating Synthetic Training Data from Processed Real Records

There are two distinct sources for generative AI training materials. The first is to create entirely fictional records—fabricating equipment, lots, personnel, and failure histories from scratch, without referencing any actual company data. For initial live training sessions, using these fully synthetic materials as the standard allows participants to focus on structuring requests and verifying AI outputs, rather than worrying about what information can or cannot be entered. The second source involves using actual logs or forms for operational purposes. These should not be referred to as “synthetic data.” Instead, their use must comply with the company’s information management policies, specifying the purpose, required data fields, storage location, and approvers. After removing or modifying identifiers, a secondary review is necessary. Simply replacing names or lot numbers is not sufficient—combinations of equipment configurations, timestamps, or customer-specific descriptions may still allow confidential information to be inferred. Processing alone does not guarantee data security.

Checklist for Preparing Training Materials

• Create Synthetic Materials First: For example, when preparing a QA nonconformance report, generate fictional products, processes, measurements, and pending judgment fields. Do not confuse this with copying an actual form and only replacing a few items—this is not the same as creating synthetic data.
• Route Real Records Separately for Approval: If there is a need to use real logs or photos in training, the information management officer must verify the purpose and scope, and check for any remaining personal data, customer information, production conditions, or drawings. If approval cannot be granted, do not use the data.
• Fix the Input Channels: Instruct participants to use only approved AI tools and accounts. Predetermine where materials will be stored, the extent of screen sharing, and whether instructors may retain copies. According to OpenAI’s Business Data Protection Policy, input and output data for Business, Enterprise, and API products are not used for model training by default, but this alone does not fulfill all confidentiality requirements for submitted materials.
• Verify in Both Thailand and Japan: Check names and addresses for variations in both Thai and Japanese scripts. Pay attention to name tags in factory photos, product labels, and equipment displays. Attach the date and approver’s name to the final version of the training materials.

By keeping these two data sources separate, both participants and instructors can clearly distinguish whether the materials used in training are “safe, synthetic practice data” or “real records that have undergone separate review.” The ETDA’s Generative AI Guidelines for Organizations in Thailand and NIST’s AI Risk Management Framework are recommended references for managing use according to organizational risk. The editorial workflow outlined here is a practical approach based on those guidelines.

Architecture of Live Online Sessions

For on-site training and skill enhancement using Generative AI, it is crucial to design live online sessions that support multiple languages and respond quickly to on-the-ground needs. Drawing on course designs from OpenAI Academy and the UNESCO AI Teacher Competency Framework, we propose the following 90-minute block example.

90-Minute Block Example: Design Sample

1. Structure Overview

• Remote Facilitator (AI specialist from the Japan or Thailand headquarters)
• On-Site Champion (local site leader, either Japanese or Thai)
• Participants (on-site staff, QA, maintenance, managers, etc.)

2. Language Support

• Separate Explanations in Japanese and Thai: Important explanations and instructions should be documented separately in both languages. Use consecutive interpretation or chat translation as needed.
• Glossary: Prepare and distribute a Japanese-Thai technical and operational term glossary to all participants in advance.

3. Sample Session Flow

4. Participation & Screen Sharing

• Hands-on AI Tool Operation on Individual Devices (using only safe synthetic data)
• Screen Sharing to review operation examples and output results together
• Breakout Rooms for small group discussions and exercises

Three-Stage Exercise Design (Example)

Stage 1: Prompt and Context Presentation

• Example: “Extract nonconformance cases from QA records and summarize the causes.”
• Participants input instructions to AI in either Japanese or Thai.

Stage 2: Output Comparison

• Groups compare AI outputs (in Japanese/Thai).
• Cross-check source records and AI outputs for omissions, errors, or misunderstandings.

Stage 3: Human Correction

• Manually correct mistranslations, summary errors, and terminology mismatches.
• Share the revised version within the group and discuss improvement points.

Detailed Role Play Exercise Examples

Role Play Exercise ①: QA Nonconformance Analysis

Objective: Develop the ability to use AI to extract causes of nonconformance and generate corrective actions from on-site QA records.

Design Example:

1. Distribute synthetic QA records (dummy data)
2. Ask AI: “Summarize the trends and main causes of nonconformance from these records.”
3. Review output as a group, correcting misunderstandings or omissions of on-site terminology
4. Discuss the validity of corrective actions and compile the final report

Worksheet Example:

• List of nonconformance items
• Notes on AI output cause analysis
• Checklist for corrections and improvements

Role Play Exercise ②: Maintenance Handover Report Creation

Objective: Acquire skills to organize maintenance handover information with AI and create clear reports.

Design Example:

1. Distribute synthetic maintenance history data
2. Instruct AI: “Summarize this information concisely for the new person in charge.”
3. Manually check the AI-generated summary for missing or mistranslated technical terms and task names
4. Review the final handover report as a group

Worksheet Example:

• Key points list from maintenance history
• AI output correction checklist
• Handover report evaluation sheet

Management-Focused Exercise: AI Utilization Risk Assessment

Objective: Enable managers to identify AI implementation risks and consider governance measures for the workplace.

Design Example:

1. Present a scenario (e.g., AI produces an incorrect QA judgment)
2. Groups list risk items (information leakage, misjudgment, workplace confusion, etc.)
3. Discuss countermeasures referencing NIST AI RMF and ETDA guidelines
4. Summarize risk management measures in checklist format

Worksheet Example:

• Risk extraction list
• Countermeasure notes
• Management action checklist

Bilingual Translation, Technical Terminology, and Misunderstanding Countermeasures

Translation and Interpretation Operations

• Prepare a Japanese-Thai Glossary in advance and distribute it at the start of the session
• Standardize On-Site Terms and Proper Nouns through consultation with local staff
• Use consecutive interpreters or AI translation tools to ensure all critical explanations are confirmed in both languages

Technical Terminology and Mistranslation Countermeasures

• Always manually check AI outputs for mistranslations or misunderstandings. Pay special attention to on-site terms (e.g., process names, equipment names) that are prone to meaning shifts between Japanese and Thai.
• Continuously update the glossary: Add new mistranslations or misuses as they are discovered.

Screen Sharing and Participation Promotion

• Live screen sharing to collectively review each participant’s outputs and point out errors
• Always-on chat and voice Q&A to reduce language barriers

Incident Response for Accidental Input of Confidential Data

• If personal or confidential data is accidentally input into an AI tool, report immediately to the IT department or information management officer
• Promptly check input history and request deletion (check in advance if the service supports this)
• Prevent recurrence by enforcing pre-input checklists and conducting regular training

Sample Action Worksheets and Checklists

Safe Data Management Checklist

• [ ] All personal and customer information is masked
• [ ] Only approved templates are used for datasets
• [ ] Prohibition of data input into unapproved tools is communicated
• [ ] Methods for managing and deleting input history are confirmed

Session Operation Checklist

• [ ] Materials and explanations are prepared in both Japanese and Thai
• [ ] Glossary is distributed in advance and updated as needed
• [ ] Screen sharing and chat participation are encouraged
• [ ] Incident reporting and response flow is communicated to all

Role Play Exercise Worksheet (Example)

• [ ] Distribution of synthetic data and masking confirmation
• [ ] Check for omissions and mistranslations in AI output
• [ ] Manual corrections and improvement points recorded
• [ ] Team review and feedback documented

Pre- and Post-Competency Assessment and 4-Criteria Rubric

To accurately measure the effectiveness of AI utilization training, it is essential to conduct competency (practical skills) assessments both before and after the program. Below is an example of a 4-criteria rubric that can be adapted for internal evaluations.

Example Rubric: Four Evaluation Criteria

1. Task Framing

– Ability to clearly define tasks for AI and design effective prompts.

1. Data Protection

– Consideration for confidentiality of input information and understanding of data usage policies for each platform.

1. Fact-Checking

– Ability to verify the accuracy of AI outputs and reference sources.

1. Improvement & Handover

– Skills in revising AI outputs, explaining results to others, and transferring tasks.

Example Scoring and Limitations

• 0 points: No experience or understanding
• 1 point: Requires instruction to perform
• 2 points: Can perform partially independently
• 3 points: Can perform and apply independently

Record a baseline for each learner in the pre-training assessment. In the post-training assessment, use the same task and scoring rubric to observe change, including whether the learner detected and corrected errors rather than relying on the final score alone.

However, AI utilization varies greatly depending on job roles and responsibilities. This rubric is just one example and may not be universally applicable to all roles and levels. For instance, in “Data Protection,” behaviors differ based on the service or contract—such as OpenAI Business Privacy—so it is necessary to design questions that reflect your actual workflows and risk assessments.

Training and Absentee Follow-up Based on Shift Work

In Thai factories, even within the same job category, employees may work different shifts. Therefore, it is not advisable to assume that everyone can attend a single live training session. The training supervisor should create a list of participants using both job category and shift as axes, and distinguish between those who need to participate in exercises and those for whom an overview is sufficient. For example, if the same 90-minute exercise is repeated for morning and evening shifts, the instructor should use the same synthetic data, assignment text, and grading sheet, while recording questions from each group separately. This allows for checking any differences in explanations between shifts. When using both Thai and Japanese languages, ensure in advance that the assignment text presents the same decision criteria to both groups, and avoid giving additional hints to only one side.

Simply sending a recording URL to absentees does not confirm whether they have learned to judge the safety of their input or recognize AI output errors. If company policy allows recordings, make sure that no personal information or real records are shown on screen, and conduct the same safe exercise on a different day after the recording is viewed. If recordings are not permitted, redistribute only the explanatory materials and synthetic data, and provide a short supplementary session and exercise. In either case, manage “training completed” and “skills confirmed” in separate columns. Site leaders should avoid forcing training to be done outside of working hours, and should first confirm when substitute personnel and devices can be arranged.

Consistent Evaluation Operations in Two Languages

When comparing Japanese and Thai language groups, if you score only on writing fluency, you end up measuring language ability rather than safe AI usage. Evaluators should decide in advance on observable behaviors for each criterion. For example, for “data protection,” can the participant identify and stop customer names, equipment identifiers, or personal information before input? For “fact-checking,” can they compare AI-generated statements with the provided source materials and flag any sentences with unclear sources? As long as the same judgment is demonstrated, the length or phrasing of the answer should not be the main focus of evaluation.

If there are two evaluators, each should grade the first few cases independently, discuss any differences, and record the reasoning in the criteria sheet (e.g., “this example is 2 points, this example is 3 points”). If a difference in meaning is found between the assignment texts in both languages, do not immediately compare participant scores; instead, either revise and re-administer the assignment, or exclude it from comparison. Even for AI-translated answers, important technical terms, negatives, quantities, and work sequences should be checked against the original. Grading results should be used not only to determine individual performance but also as material for deciding which explanations to emphasize in future training materials.

Approval Process Before Applying Exercise Outputs to On-Site Documents

Draft handover notes or SOP proposals created during training, even if they appear well-made, should not be directly implemented in actual operations. Label exercise outputs at the end of training as “for practice with synthetic data,” and store them separately from official procedures. When expanding to actual business improvements, switch to a phase using separately approved real data, and have those responsible—such as document controllers, equipment managers, and safety officers—compare the content with original materials and actual equipment. For example, in maintenance procedures, confirm that equipment models, isolation steps, and inspection conditions match the official version; if not, do not adopt the AI-generated text.

Approval records may include the version of the original material, sections where AI was used, the person who made revisions, the approver, and the effective date. These are operational examples proposed by our editorial team and should be adjusted according to your company’s document management policies. In training evaluation, consider not only “whether a draft was quickly created using AI,” but also “whether inappropriate content was identified and the reason explained,” to better confirm the decision-making skills needed on site. The key is to clearly define the boundary between human verification/approval and the transition from training completion to actual operation.

Training supervisors should decide in advance the “ownership” and “scope of use” for documents created during exercises. Distinguish between materials retained by the instructor, training materials kept in-house, and individual participant responses, and list their storage locations and viewers. The same applies to chat logs and screen-sharing records from online meetings. If using external instructors, confirm at the contract stage when to delete training materials and responses after the exercise, and whether they may be reused as examples. If it is necessary to retain responses for evaluation, remove names not needed for grading and store them in a location accessible only to evaluators. These are operational checks to be determined according to your company’s information management policies, not indications of specific legal retention periods.

2–4 Week Follow-up and Pilot Testing

After training, a 2–4 week follow-up period is recommended to assess skill retention and identify ongoing challenges in real work settings. Suggested approach:

• 2 weeks after training: Use participant surveys and short tests to check on-the-job application.
• 4 weeks after training: Conduct interviews with team leaders and managers to extract remaining issues.

For pilot (trial) implementation, start with 1–2 departments or up to 20 participants for a period of 2–4 weeks. Example evaluation criteria:

• Achieve an average score of 2 or higher on the rubric
• Generate at least 3 cases of AI utilization in actual operations
• Prevent confidential-data entry in every exercise and document the fact-checking steps

The participant count, timeline and numerical targets above are planning examples; agree them in advance according to task risk and available staff. Do not treat accidental disclosure of confidential information as an acceptable percentage: define a separate reporting and containment process. Use pilot results to decide whether to expand the program or integrate it into business processes.

Comparison of Online, Onsite, and Recorded Training Formats

AI training can be delivered in live online, onsite (in-person), or recorded (on-demand) formats. The optimal choice depends on your workplace environment and learning objectives.

• Live Online: Enables real-time Q&A and demonstrations. Allows simultaneous participation from multiple locations, which is ideal for companies with sites across Thailand and Japan, or when scheduling across time zones.
• Onsite: Best for hands-on practice and group work in person. Often chosen when on-site IT environments or security requirements are strict.
• Recorded: Effective for repeated learning and review. Offers flexibility for participants’ schedules, but may be limited in immediate Q&A or practical business discussions.

For example, OpenAI Academy Courses emphasize hands-on exercises and review features aligned with real business tasks. However, with recorded-only formats, it can be difficult to achieve interactivity or address individual workplace challenges. Selecting the right training format to match your company’s objectives and constraints is crucial.

Example Questions for LIVE Online Training Procurement (Beyond Price and Standard Vendor Criteria)

1. Which industries and job roles do your AI utilization case studies cover?
2. Is bilingual support available in Japanese and Thai?
3. Can the training be customized to meet our company’s security requirements (e.g., restrictions on specific cloud services)?
4. What are your policies on recording live sessions and sharing training materials?
5. How is participant privacy and personal data handled?
6. How are certificates of completion and training records issued and managed?
7. Is feedback provided on practical assignments, and what post-training support is available?
8. How do you conduct post-training evaluation and propose improvements?

For more detailed procurement and RFP creation guidelines, please refer to the Generative AI Training Provider Selection Guide (Thailand 2026 Edition).

Implementation Checklist (with Responsible Roles and Example Deliverables)

FAQ

Q1. Can online generative AI training be delivered in Japanese and Thai?

A. Language support varies by provider and training format. Confirm whether Japanese and Thai instructors or interpreters are available, and which languages will be used for exercises, materials and questions.

Q2. Can learners join online generative AI training from a mobile device?

A. Mobile participation depends on the delivery platform and exercise tools. Ask the provider which devices and browsers are needed for screen sharing, hands-on work and reading materials.

Q3. Will recordings and materials be available for later review?

A. Policies on providing recordings and materials differ by vendor. Also consider your company’s information security policies and participant privacy when making arrangements.

Q4. Are certificates of completion or proof of attendance issued?

A. Some training programs provide certificates or training records. These can be used for internal HR evaluations or to support management system development such as ISO/IEC 42001:2023 (ISO/IEC 42001 listing), but attending training itself is not a requirement for certification.

Q5. How is participant privacy and data protected?

A. Personal information and statements during training are managed in accordance with the vendor’s privacy policy and terms of use. For business use of OpenAI, for example (OpenAI Business Privacy), confirm the scope of data usage and retention periods in advance.

Q6. How can we evaluate the effectiveness of the training?

A. It is recommended to combine multiple indicators, such as pre- and post-training rubric assessments, follow-up surveys, and the number of AI use cases generated in practice.

Conclusion

Internal training for generative AI utilization should go beyond simple knowledge transfer—it is important to design programs that also visualize and reinforce practical skills, and integrate risk management such as data protection and fact-checking. By combining assessment rubrics, follow-up, and pilot testing, you can build AI capabilities rooted in real workplace needs. Please tailor your training design and procurement to match your company’s business challenges and security requirements.

If you would like to discuss evaluation design for AI training or explore use cases tailored to your business, please feel free to contact us via the TOMAS TECH Contact Form. You can also get in touch while still exploring the approach.

Reference Information

1. ETDA Thailand, Generative AI Governance Guideline for Organizations PDF
2. NIST AI RMF Generative AI Profile, July 2024, updated 2026
3. OpenAI business privacy
4. WEF Future of Jobs 2025 press release
5. OpenAI Academy courses
6. UNESCO teacher AI competency framework
7. ISO/IEC 42001:2023 standard listing