AI in World Language Learning: Customizing Instruction for Success

Outline

1. Introduction to AI-Driven Differentiation (5 minutes)

- Content: Quick overview of the importance of differentiation in language acquisition, tied to Stephen Krashen’s Comprehensible Input Hypothesis.
- Engagement: Start with a rhetorical question: “How can we ensure that every student, regardless of level, receives tailored instruction?” This sets the stage for AI as the solution.

2. Content Simplification with AI (5-7 minutes)

- Content: Demonstrate how AI tools like ChatGPT simplify reading/listening passages, adjusting complexity for different levels while creating compelling input to engage learners.
- Engagement: Rapid demo of AI modifying content, showing how teachers can easily adjust text complexity for their students.
- Process: Offer actionable tips on using ChatGPT and MagicSchool to personalize content quickly and effectively.

3. Targeted Grammar and Vocabulary with AI (5-7 minutes)

- Content: Show how AI tools such as ChatGPT, MagicSchool and QuestionWell create tailored grammar and vocabulary exercises, scaffolding support for beginners and providing advanced tasks for proficient learners.
- Engagement: Quick demo of scaffolded role-play conversations using MagicSchool, and a fast breakdown of creating grammar exercises with QuestionWell.
- Process: Provide practical, quick-start strategies for using these tools in class to meet individual student needs.

4. Real-Time Feedback and Adaptive Learning (5-7 minutes)

- Content: Showcase how ChatGPT and MagicSchool provide instant, personalized feedback on student writing and speaking exercises, adapting based on performance.
- Engagement: participants engage with AI chatbots to practice conversations in the foreign language, highlighting the efficiency and accuracy of real-time feedback. Participants will also learn how to use AI to deliver instant feedback based on the student’s performance in writing and speaking assignments.
- Process: Encourage attendees to try these tools immediately, explaining how feedback can be adapted to each student’s proficiency level.

5. Conclusion and Actionable Takeaways (5 minutes)

- Content: Recap how AI supports differentiation across content simplification, targeted grammar/vocabulary, and real-time feedback.
- Engagement: Close with a strong call to action: “How will you use AI tools like ChatGPT, QuestionWell, and MagicSchool to enhance differentiation in your classroom?”
- Process: Distribute a concise digital handout with the top 3 AI tools mentioned (ChatGPT, QuestionWell, MagicSchool) and key implementation steps.

Supporting research

1. Stephen Krashen’s Comprehensible Input Hypothesis:
Krashen’s Comprehensible Input Hypothesis is foundational to language acquisition theory, emphasizing that learners progress when exposed to input slightly above their current proficiency level (i+1). This directly supports the need for differentiated instruction to ensure comprehensible input is tailored to individual learner levels.

Reference: Krashen, S. D. (1982). Principles and Practice in Second Language Acquisition. Pergamon Press.

2. Compelling Input and the Affective Filter:
Krashen expanded on Comprehensible Input with the idea of Compelling Input, where input is not only comprehensible but also interesting and engaging to the learner. This reduces the affective filter: the emotional barrier (for example, anxiety, lack of motivation) that can hinder language acquisition. By considering students’ preferences and personal interests, educators can lower this filter, making input more effective.

Reference: Krashen, S. (2011). The Compelling (not just interesting) Input Hypothesis. The English Connection, 15(3), 1-3.

3. Affective Filter Hypothesis:
Krashen’s Affective Filter Hypothesis explains how emotional factors like motivation, self-confidence, and anxiety affect language acquisition. Students are more likely to acquire language when they are emotionally engaged and their affective filter is low. This supports the need to use AI tools that can tailor content to student preferences, increasing engagement and reducing anxiety.

Reference: Krashen, S. (1985). The Input Hypothesis: Issues and Implications. Longman.