AI TOTALLER Framework

AI TOTALLER for ELT: Prompt Engineering AI Companions in VR/VWs - an Evaluation Form

31/1/2025

Enhancing the AI TOTALLER Framework: The Importance of the Evaluation Form (available on this link)

As language educators, our goal is to create effective and engaging learning experiences for our students. The integration of innovative frameworks, such as the AI TOTALLER with API Keys into language teaching in Virtual Reality (VR) and Virtual Worlds (VWs) has the potential to transform how we approach language learning. However, to ensure that this framework meets its intended objectives and continues to evolve, it is crucial to gather feedback systematically.

Applying the 'AI TOTALLER' Model for lessons in VR/VWs with AI Companions

In order to apply the Framework in your teaching set-up, you need to make sure you:

Understand the Model
Review the Examples provided
Create an immersive prompt with some or all of the elements of AI TOTALLER
Insert the prompt into your AI Companion(s) and review their responses
Share your review together with the link to your activity in any of the VR/VW destinations we have explored, on our Padlet (AI Companion and Prompt card based on TOTALLER), together with your feedback

Purpose of the AI TOTALLER Evaluation Form

The AI TOTALLER Evaluation Form is an essential tool for me, as the developer of the framework, to gather insights that will help refine and enhance the AI TOTALLER for English Language Teaching (ELT) in VR/VWs with AI companions. Here are the key purposes of this evaluation form:

Assess Effectiveness

The form allows for feedback collection on how well the AI TOTALLER framework supports language learning objectives. Evaluating aspects such as engagement, clarity, and adaptability helps determine the framework's overall effectiveness.

Identify Strengths and Weaknesses

By gathering detailed responses from educators and students, I can pinpoint which components of the framework are working well and which areas may need improvement. This information is crucial for refining the framework.

Promote Continuous Improvement

The feedback obtained through the evaluation form encourages an iterative approach to development. Insights gained from evaluations will inform adjustments to enhance future iterations of the AI TOTALLER framework.

Support Data-Driven Development

The evaluation form provides quantifiable data that I can use to support decisions regarding updates and enhancements to the framework. This data-driven approach ensures that changes are based on actual user experiences.

Foster Collaboration

Sharing evaluation results with educators can foster collaboration and discussion about best practices in using the AI TOTALLER framework. Collective reflection can lead to innovative ideas and improved teaching methods across various contexts.

How to Use the AI TOTALLER Evaluation Form

To effectively utilise the AI TOTALLER Evaluation Form in your classroom:

Distribute the Form: Share the evaluation form with your teachers/students after implementing the AI TOTALLER framework in your lessons.

Encourage Honest Feedback: Emphasise the importance of providing honest and constructive feedback to help improve the framework.

Analyse Results: After collecting responses, I will analyze the data to identify trends and areas for improvement.

Make Adjustments: Use insights from the evaluation to refine the AI TOTALLER framework, making necessary adjustments to enhance its effectiveness.

The AI TOTALLER Evaluation Form is a vital resource for me as I work to develop and enhance this framework for English Language Teaching in VR/VWs with AI companions. By systematically assessing its impact on language learning, I can ensure that it remains relevant, effective, and aligned with educators' needs.

To access and use the AI TOTALLER Evaluation Form, please click here.

Please inform me if the AI TOTALLER Evaluation Form has been used to gather feedback. This information is invaluable for the ongoing development and enhancement of the framework, ensuring it meets the needs of educators and learners effectively.

Thank you for your support in this ongoing development process, which aims to create impactful language learning experiences for educators and learners alike!

© Helena Galani - All rights reserved

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Utilising the AI TOTALLER Framework: Prompt Engineering of AI Companions for Language Teachers in VR and VWs

15/8/2024

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Drawing from my daily teaching practice with learners of all ages and various formal exam preparations, I extensively leverage Artificial Intelligence Personas in Virtual Reality (VR) and Virtual World (VW) environments. This hands-on experience led me to develop AI TOTALLER, a framework designed to help language educators create prompts for the AI Companions they build tailored to their class objectives.

As language educators seek innovative methods to enhance learning, the integration of VR, VWs together with AI Companions offers exciting possibilities. AI TOTALLER, developed by Helena Galani, provides a structured approach to prompt engineering, enabling teachers to design effective AI NPC interactions in virtual environments.

This post will explore the AI TOTALLER framework’s value for English language teachers and educators of other languages, demonstrating how it can enhance learning experiences through well-crafted AI prompts.

Understanding AI TOTALLER for prompt engineering

The AI TOTALLER mnemonic encapsulates essential parameters to consider when creating prompts for AI companions in immersive environments. Each component of the framework is designed to ensure that interactions are engaging, relevant, and tailored to learners’ needs:
T - Tone and Emotion Set the desired emotional tone for your AI companion's interactions. This consistency helps create an engaging atmosphere. For example, an NPC in a VR fitness world might adopt a motivational tone while guiding users through exercises.
O - Output Formatting for Optimized Multimodal Content Design prompts that align with specific language goals, utilizing various output formats—text, audio, visuals, or interactive elements. For instance, a virtual chef NPC could provide step-by-step cooking instructions while displaying visual aids.
T - Targeted Design Align prompts with the specific activities and objectives of the VR/VW experience. This clarity ensures that the NPC’s role is relevant to learners' actions. For example, an AI language tutor might teach new vocabulary based on the user's proficiency level.
A - API for Adaptive Strategies Utilise APIs to enhance NPC responses with real-time data, allowing for dynamic interactions. An NPC could adjust its explanations based on the learner’s progress or retrieve current information relevant to a discussion topic.
L - Level-Based Differentiation Adapt NPC behavior according to individual learner levels and preferences. This internal differentiation allows learners of varying proficiencies to engage with material suited to their unique needs—beginners might focus on simple sentences while advanced learners tackle complex dialogues.
L - Logical Structure and Learner-Centered Choices Provide clear guidance through logical steps in interactions. This structure helps learners navigate tasks effectively. For instance, a virtual tour guide could lead users through a museum while explaining exhibits in order.
E - Experiential Engagement Focus Encourage user interaction by creating prompts that elicit questions or offer choices. An NPC in a VR city might ask tourists if they want to explore a new area or take a quiz after a tour.
R - Role Specification for Reflective Opportunities Clearly define the NPC’s role to facilitate understanding of interaction styles and purposes. Incorporate opportunities for self-evaluation and reflection, such as an NPC historian sharing interesting facts about historical events while inviting learners to discuss their thoughts.

AI TOTALLER Specifics for ELT

In addition to the general framework, here are specific parameters to consider when designing your AI NPC prompts for English Language Teaching (ELT):

Task: Define the type of task and activity learners will engage in, such as quests or role-plays.
Objective: Clarify the goal of the responses and what lesson objectives are being targeted.
Target: Identify who the NPC communicates with and what information is crucial to clarify.
Actor: Specify who the NPC acts as and what type of behavior or character they embody.
Learner Level: Consider existing knowledge and proficiency levels of learners.
Lesson Aims: Outline expected outcomes from each lesson.
Expectations of Learner Pragmatic Knowledge: Anticipate how learners are expected to react, potential mistakes they might make, and methods for correction.
Register and Style: Determine the politeness level and formality of the NPC's language.

These specifics provide additional guidance for educators looking to create effective interactions within VR/VWs.

The Importance of Feedback Mechanisms

Incorporating feedback mechanisms within the AI TOTALLER framework is crucial for effective learning. By providing real-time corrections, personalised suggestions, and positive reinforcement, AI companions can significantly enhance learners' language acquisition processes.

While AI Companion API keys are not feedback mechanisms themselves, they play a crucial role in enabling effective feedback mechanisms by allowing AI companions to access real-time data and personalize interactions. This capability enhances the overall learning experience by providing timely and relevant feedback to learners.
Feedback mechanisms are enhanced in the following ways: Real-Time Feedback:
By using APIs to access current data (e.g., language usage examples or pronunciation guides), AI companions can provide immediate and contextually relevant feedback during interactions.
Personalized Learning:
APIs can help AI companions track learner progress and preferences over time. This information allows for more personalized feedback that addresses specific strengths and weaknesses.
Enhanced Interaction:
APIs can enable AI companions to pull in multimedia resources (like videos or articles) that provide additional context or examples, enriching the feedback provided to learners.
Data-Driven Insights:
Using APIs to collect and analyse learner data allows educators to gain insights into student performance patterns. This information can inform the feedback given to learners and help tailor instruction accordingly.

Insight

To ensure my prompts are best designed for my ELT learners, I implement features of the AI TOTALLER Framework.

The AI TOTALLER Framework was first introduced at VWMOOC 2024 on August 14th during my session titled "AI-Powered Teaching Quest with NPCs in Second Life." You can watch it here.

As Lead Moderator, I presented practical applications of utilising AI TOTALLER and for engineering your class prompts in VR/VWs such as FrameVR, Kitely/OpenSim and Second Life, at the Electronic Village Online 2025, a TESOL International CALL IS Session, "Immersive AI Companion Design" in January - February 2025.

It illustrates the content of the AI Companions I showcased at the Immersive Quest around EduNation III, Second Life (presented at the VWBPE Conference 2024) with three main characters the Dragon, Lexis and Captain, and the AI Agents built for my learners in FrameVR, ELT Treasure Island/Kitely and WondaVR.

The AI TOTALLER framework offers language teachers a robust foundation for designing engaging and effective prompts for AI companions in VR and VWs. By focusing on tone, output formatting, targeted design, adaptive strategies, differentiation, logical structure, experiential engagement, and role specification—along with specific parameters tailored for ELT—educators can create immersive learning experiences that cater to diverse learner needs.
As we embrace these innovative teaching tools, let us harness the power of AI TOTALLER to transform language education worldwide. For more insights into implementing this framework in your teaching practice, stay tuned for further resources and examples!
For additional information on the AI TOTALLER framework and its applications in TESOL, visit Helena Galani's resource page.

For any references or citations regarding the AI TOTALLER framework and its applications in language teaching, please use this site https://www.agogepaedeia.com/ai-totaller-framework and quote me as:
Helena Galani, Developer of the AI TOTALLER Framework for prompt Engineering in Language Teaching

©Helena Galani - All rights reserved

Edited January 2025

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Presenting "AI-Powered Teaching Quest with API Keys and NPC chatbots in Second Life" at the 10th annual Second Life MOOC

14/8/2024

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On 14th August 2024, I presented the 'AI-Powered Teaching Quest with API Keys and NPCs in Second Life', at the 10th annual Second Life MOOC.

Discover an innovative Quest created by Helena Galani in EduNation, Second Life, tailored for English learners of all ages and levels! Using OpenAI API Keys, NPC chatbots offer engaging, realistic conversations to enhance language skills. EduNation is inhabited by Residents with extensive experience in teaching in virtual worlds, ensuring a rich, educational experience. Don't miss this cutting-edge educational adventure. Explore new teaching horizons and join the fun!

Helena presented her AI TOTALLER for prompt engineering with the use of API Keys and AI Personas.

Location: Helena’s Isle, on EduNation III, Second Life http://maps.secondlife.com/secondlife...

The Presentation recording is available on You Tube.

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Helena Galani
- MA ELT University of Nottingham,
- R.S.A. Diploma University of Cambridge/UCLES

Please provide a citation for any content you use from this webpage.

I am an Applied Linguist with extensive experience in Teaching English to Speakers of Other Languages (TESOL), Teaching English as a Foreign Language (TEFL), and English for Business and Academic Purposes, both in Greece, the UK, and online. My expertise lies in English Language Teaching enhanced by online applications, Artificial Intelligence, Virtual Reality, and Virtual Worlds.

Member of:
- Euroversity Association,
- TESOL International
- TESOL Greece,
- Scientific Association for the Promotion of Educational Innovation
- European Association of Language Testing and Assessment,

AI TOTALLER for ELT: Prompt Engineering AI Companions in VR/VWs - an Evaluation Form

Utilising the AI TOTALLER Framework: Prompt Engineering of AI Companions for Language Teachers in VR and VWs

Presenting "AI-Powered Teaching Quest with API Keys and NPC chatbots in Second Life" at the 10th annual Second Life MOOC

Helena Galani
- MA ELT University of Nottingham,
- R.S.A. Diploma University of Cambridge/UCLES

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AI TOTALLER for ELT: Prompt Engineering AI Companions in VR/VWs - an Evaluation Form

Utilising the AI TOTALLER Framework: Prompt Engineering of AI Companions for Language Teachers in VR and VWs

Presenting "AI-Powered Teaching Quest with API Keys and NPC chatbots in Second Life" at the 10th annual Second Life MOOC

Helena Galani- MA ELT University of Nottingham,- R.S.A. Diploma University of Cambridge/UCLES

Archives

Categories

Helena Galani
- MA ELT University of Nottingham,
- R.S.A. Diploma University of Cambridge/UCLES