Video summary

ELTLT 2026 - MAIN ROOM (PLENARY SESSIONS)

Main summary

Key takeaways

Educational

Main ideas, concepts, and lessons (ELTLT 2026 plenary sessions)

Conference opening: purpose, theme, and formalities

  • The event is the 15th online international conference on English Language Teaching, Literature, and Translation (ELTLT/ELT-LT) 2026.
  • The theme emphasized:
    • “Responses of humanities to global challenges”
    • Focus on English language teaching, linguistics, literature, and translation for sustainable development.
  • A formal opening process is conducted, including:
    • Participant microphone muting request for opening sessions
    • Welcome speeches and official opening
    • Indonesian national anthem and related ceremony elements
    • Islamic prayer (participants of other faiths follow their own tradition)

Plenary 1 (Keynote + discussion): Informal education and “looking sideways” (Dr. Leslie Moore)

Core thesis / lesson

  • Innovation in ELT should not be limited to formal institutions (schools, universities, curriculum developers, governments, testing bodies, tech companies).
  • Educators should “look sideways” to find innovation already happening in informal educational spaces.
  • Learning is framed as an “educational ecology”:
    • Schools matter, but families, communities, religious institutions, and digital networks also shape language learning.

Key concepts

  • Informal—according to whom?
    • Many “informal” settings are actually highly structured (goals, teachers, expectations, traditions).
    • The “informal” label often reflects formal institutions’ perspectives, not participants’ experiences.
  • Culture organizes learning
    • In formal settings culture is often treated as “content,” but in informal settings culture shapes:
      • relationships, authority, participation, language use, and identity
  • Learning through participation
    • Language learning happens through participation in social life (not just instruction).
  • Repertoires of practice
    • Communities develop recurring activities (e.g., storytelling, recitation, conversation, collaborative work).
    • Competence grows through repeated participation—ask what learners do and with whom, not only what they know.

Three main informal settings showcased (with takeaways)

  1. Faith literacy

    • Not only memorization; it involves:
      • attention to form, repetition, corrective feedback
      • identity and community membership
      • multilingual resources used strategically
      • metalinguistic/linguistic awareness
    • A major interpretive insight (credited to “Zulfa”):
      • Religious learning can be understood as “caring” vs “mastery”
      • Mastery implies an endpoint; care implies an ongoing relationship among teacher, learner, and text.
    • Additional insights:
      • embodiment (values/caring conduct)
      • multilingual pedagogies functioning as resources rather than compartmentalized systems

  2. Community learning centers

    • Example: a center in Central Java (Marwa/Tanpa clarity in transcript)
    • Key outcome:
      • storytelling can move learners from avoided speaking to gaining confidence, voice, and agency
    • Emphasis:
      • literacy is most effective when tied to meaning, identity, experience, and community voice

  3. “Virtual village” (intergenerational learning across borders)

    • Example: Indonesian Muslim families in the U.S. supporting children’s religious education and language learning via video calls with elders in Indonesia.
    • Core idea:
      • technology sustains relationships rather than replacing community
    • Innovation type:
      • adaptation, not replacement

How the discussion extended (Q&A themes)

  • Religion-based informal education in the U.S.
    • Dr. Moore says religious communities commonly include informal education practices (sometimes including sacred/heritage language).
  • Government support
    • Religious education tends to be privately handled in the U.S.; formal/informal status differs by context.
  • Intergenerational learning in science centers
    • In Dr. Moore’s work:
      • science outreach aims to welcome multilingual families and avoid treating them as “lab rats”
      • uses participatory, asset-based approaches
      • emphasizes that informal science need not be only “for English”; it can support family/home languages too
  • Practical classroom question
    • A participant from West Kalimantan asked how to use informal/local activities in English learning with limited resources.
    • Dr. Moore’s response emphasized:
      • start/end with songs, use language games
      • draw on children’s language play traditions
      • incorporate familiar playful language practices to create motivation and meaningful learning

Plenary 2 (Keynote + discussion): AI in ELT in the post-digital era (Dr. Sutsuang Yudana)

Core question framework

Participants are prompted to think:

  • Is AI a friend or pedagogical partner?
  • What part of learning must remain visibly human even when AI is available?

What “AI in ELT” means

AI is described as an ecosystem of technologies, including:

  • adaptive platforms using learner data to adjust difficulty
  • NLP tools for grammar/vocabulary/style/coherence feedback
  • conversational AI for dialogue/roleplay/Q&A practice
  • speech recognition for pronunciation/fluency/intonation
  • generative AI for tutoring, brainstorming, drafting, and material generation

Important pedagogical caution:

  • Don’t choose tools by brand; choose by pedagogical function:
    • What learning needs does it address?
    • What can learners practice?

Reported major benefits (patterns across the literature)

  1. Access
    • learners can get explanations and practice outside class time
  2. Immediacy
    • feedback/support while the task is still “active in the mind”
  3. Personalized learning
    • adjusted pace/content/difficulty by learner need

Benefits by skill (examples from the talk)

  • Speaking: conversational practice; speech recognition feedback (pronunciation/fluency/intonation)
  • Writing: grammar/style pattern detection; drafting + revision cycles
  • Reading: adaptive text difficulty; vocabulary explanation; summarization; comprehension support
  • Vocabulary: spaced repetition, contextual examples, quizzes/gamification

Shift from “friend” to “pedagogical partner”

The keynote argues AI helps most when learners gain:

  • agency (choose when/how to practice; repeat privately; move from passive to active learners)
  • feedback and revision without embarrassment
  • progress tracking and gap identification
  • appropriately adjusted challenge levels

Risks and challenges (grouped into four areas)

  • Pedagogical
    • overreliance reduces critical thinking; polished outputs may avoid learning processes
  • Ethical
    • privacy/personal data
    • academic integrity and ownership of AI-assisted work
    • unequal access (digital divide)
  • Professional
    • teachers need training and skill to orchestrate AI responsibly
  • Trust/accuracy
    • AI can give confident but incorrect/bias outputs

Practical instructional methodology (detailed steps described)

To use AI as a pedagogical partner, Dr. Sutsuang recommends thoughtful learning design:

  • Select tools based on real learning needs
  • Design activities connecting AI use to learning outcomes
  • Teach prompting and interpretation
    • learners must be guided so prompting is purposeful
  • Require documentation of the AI workflow
    • students submit prompt records + AI responses
  • Use AI outputs as input for higher-order tasks, not as final submissions
    • Example given for an argumentative essay:
      • students develop their own position
      • use AI to generate counterarguments
      • students fact-check and annotate counterarguments
      • students revise essays using teacher criteria and improve evidence/language/organization
      • students submit the AI prompt/response dialogue as evidence of process
  • Use rubrics and (suggested) peer feedback
    • discussion emphasized teacher-made rubrics and qualitative evaluation first, rubric refinement later

Q&A highlights

  • Young learners
    • Dr. Sutsuang recommends not dumping AI responsibility fully onto parents
    • prefer controlled in-class use or structured turns with guidance to avoid making AI a burden
  • Feasibility under resource gaps
    • addressed through teacher training and practice with AI (learning how AI works + how to prompt appropriately)
  • Preventing misuse
    • emphasis on guided use, documentation, and assessment of learning process rather than only final product

Plenary 3 (Recorded due to absence): English literacy recovery in rural secondary schools (Prof. Suyansa Bisuanto)

Core purpose

  • Present a community-powered, school-embedded learning recovery model for rural learners in Tuaran, Sabah.
  • Goal: move learners from learning loss to learning power/positive participation (phrased in transcript as “learning po/learning power”).

Main argument

Recovery becomes possible when three elements meet the learner’s needs:

  1. Evidence
    • diagnostic data prevents guessing; identifies specific skill gaps
  2. Relational mentoring
    • addresses confidence, identity, and participation (not only cognition)
  3. Local ownership
    • sustainability requires routines embedded in schools and local authorities

Methodology / intervention structure (detailed)

  • Model movements (3 phases)

    1. Diagnose
      • identify precise learning problems via baseline testing/diagnostic assessment
    2. Mediate humanely
      • deliver guided practice via trained graduate tutors
    3. Sustain locally
      • embed routines with school teachers and the local education authority

  • Program design details

    • school-embedded recovery model
    • involves:
      • 4 schools
      • target ~320 learners
      • 16 graduate tutors
    • duration ~14 months

  • Assessment & evidence strategy

    • baseline diagnostics (e.g., MCQ and writing mean scores; many “high risk” learners)
    • endline assessment to measure effectiveness
    • interviews for learner perceptions
    • tutor feedback and reflection

  • Key principle

    • Success is not just remediation or worksheet completion; it is:
      • restoration of agency and learner dignity
      • improved attendance and participation

  • Core “unit of change”

    • not worksheets, but the learner’s next successful participation

  • Research/practice partnership logic

    • asks:
      • what works for whom
      • through which mechanisms
      • under what conditions

Plenary 4 (Keynote + discussion): Literary translation, narrativity, and AI (Dr. William James Gatherer)

What the talk argues

  • Literary translation and translated literature involve narrativity—text “feeling like a story” and producing meaning.
  • Translators must consider complex “ecosystems” around the text:
    • author context, philosophical landscape, cultural/political positioning
    • publisher/intermediary constraints
    • translation as narrative meaning production—not just message transfer

Key concept: translatorial narrativity

  • Translations have their own narrativity; it cannot be fully hidden.
  • Even “pseudo translations” (invented source poets/texts) can produce translation-like narrativity.
  • Because translations involve multiple origins and agents, they create narrative effects differently from originals.

How AI changes translation/narrativity (major claims)

  • AI translation research often focuses on where AI fails (e.g., cultural nuance), but Dr. Gatherer shifts attention to:
    • what AI does to narrativity itself
  • AI introduces a new “agent” (human-machine interface), producing posthuman narrative effects:
    • narratives can be perceived as lacking direct human authorship
    • this changes the status and value of narrative meaning production

  • He critiques “full automation” narratives:

    • technological determinism is exaggerated; real application and impacts are complex

  • AI and creativity:

    • people tend to accept AI-generated ads more than AI-generated literature; trust/value declines for highly replicable “creative” output

  • Sustainability and ethics:

    • AI has environmental cost concerns
    • IP/data consent concerns are significant

Q&A themes

  • Justifiability of AI-assisted creative writing
    • permissible to use AI, but warns about long-term value/status changes if AI-generated narratives become easily reproducible
  • Handling culture-specific references in translation
    • translation is a spectrum of choices and advocacy: translators can expose/source culture rather than always smoothing it for target audiences
  • Knowledge of source culture
    • translation can be done without full cultural knowledge, but translators remain responsible for choices and the resulting text effects

Plenary 5 (Keynote + Q&A): Translation practices addressing SDGs for sustainable development in Indonesia (Prof. Rudy Hartono)

Core proposition

  • Translation is a bridge enabling global knowledge/standards to become local action, supporting Indonesia’s sustainable development goals (SDGs).

Conceptual framework

Uses Jakobson’s categories of translation:

  • Interlingual: render meaning between languages (e.g., international policy into Bahasa Indonesia)
  • Intralingual: reword/clarify within the same language (e.g., simplify technical regulations for public understanding)
  • Intersemiotic: translate between sign systems (e.g., climate data → infographic; text → audio/visual; into sign language formats)

Why translation matters for SDGs in Indonesia

  • Without translation/localization, the gap grows between global commitments and local understanding.
  • Addresses SDG domains including:
    • SDG 4 quality education
    • SDG 3 good health and wellbeing
    • SDG 13 climate action
    • SDG 8/9 growth/work/innovation
    • SDG 5 and 10 equality and inclusion
    • SDG 16 peace/justice/strong institutions

Sector examples provided

  • Education
    • localized curriculum materials, textbooks, assessment frameworks; open educational resources in Bahasa
  • Health
    • public health guidance, vaccine/medicine information, medical protocols, localized health campaigns
  • Climate action
    • translating IPCC reports and disaster warnings to enable local stewardship and timely response
  • Inclusion & governance
    • adapted materials for minority languages and disability access; legal aid and harmonization for smoother compliance
  • Case study mentioned:
    • e-reporting and regulatory compliance via translation processes
    • preserving linguistic heritage and indigenous knowledge through translation and localization

Translation + technology caution

  • Supports translation technology (MT, terminology tools, drafting support) but insists on:
    • human post-editing
    • human responsibility for nuance in legal/cultural/idiomatic contexts

Capacity building and institutional ecosystem

  • Persistent challenges:
    • shortage of specialized translators in non-urban areas
    • limited standardized quality assessment
    • insufficient mentorship pathways
  • Proposed ecosystem supports:
    • university training/translation capacity
    • certification standards
    • partnerships with other institutions across borders

Recommendations (as summarized in the talk)

  • Improve translation-enabled development by:
    • ensuring/standardizing translation quality (including in accreditation instruments)
    • expanding translator/interpreter training pipelines
    • invest in responsible government translation
    • standardize plain-language guidelines for regulatory and e-reporting communication
    • strengthen international academic partnerships

Methodologies / actionable instruction lists explicitly presented

Dr. Leslie Moore: “Look sideways” (conceptual method to find ELT innovation)

  • Shift innovation search away from formal institutions and ask:
    • What educational work already exists in families, communities, religious institutions, and digital networks?
  • Treat “informal” as “informal according to whom”:
    • examine participant perspectives and internal structure
  • Analyze learning as participation:
    • ask what learners do and with whom (not only what they know)
  • Use “repertoires of practice” lens:
    • identify recurring community activities that build language competence
  • Connect literacy to meaning:
    • prioritize voice, agency, identity, and relevance to learners’ lived experiences

Dr. Sutsuang Yudana: AI as a pedagogical partner (practical classroom procedure)

  • Select AI tools by pedagogical function:
    • identify learning needs and targeted practice opportunities
  • Design AI-connected tasks aligned to learning outcomes
  • Guide students in prompting and interpreting AI output (don’t treat prompting as self-teaching)
  • Assess learning through process documentation:
    • require submission of prompts used + relevant AI responses
  • Example procedure for argumentative writing with AI:
    • students craft original position
    • AI generates counterarguments
    • students fact-check/annotate counterarguments
    • students revise essay using teacher criteria
    • students submit prompt/response dialogue as part of the assignment record

Prof. Suyansa Bisuanto (recorded): learning recovery model (operational framework)

  • Phase 1: Diagnose precisely (baseline evidence)
  • Phase 2: Mediate through relational mentoring (guided practice with tutors)
  • Phase 3: Sustain locally (embed routines with school + local education authority)
  • Evidence strategy:
    • baseline + endline measures, plus learner voice and mentor/school reflection
  • Success criteria:
    • literacy gains, attendance, confidence, participation
    • lasting capability after external grant ends

Speakers / sources featured (identified in the subtitles)

Main featured speakers (plenary/keynotes, moderators, MCs)

  • Ashandi (host/MC, conference opening)
  • Dr. Leslie Moore (Ohio State University, keynote Plenary 1)
  • Miss Sri Sumarani (moderator for Dr. Leslie Moore session)
  • Dr. Eko Rahario (vice dean; planned opening remarks; speech delivered)
  • Dr. Reini Susanti Wulandari (chair of organizing committee; welcoming speech)
  • Mr. Daw Jandono (led Islamic prayer)
  • Dr. Eoro M / Dr. Eko Rahario / Dr. Rahayupji Haranti / Dr. July (multiple university coordinators referenced; names appear with transcription errors)
  • Dr. Noia Tricanti (moderator for Plenary 2)
  • Dr. Sutsuang Yudana (Naresuan University, keynote Plenary 2 on AI and ELT)
  • Professor Dr. Suyan (Suyansa) Bisuanto (University Malaysia Sabah; recorded Plenary 3)
  • Mr. Bambang Purwanto (moderator for Dr. Gatherer session)
  • Dr. William James Gatherer (University of Queensland, keynote Plenary 4 on literary translation and AI)
  • Dr. Rohani (moderator for Plenary 5)
  • Professor Dr. Rudi Hartono (Rudy Harono / Rudo / Rudiardov) (Universarang/Universitas Samarang as referenced; keynote Plenary 5 on translation and SDGs)
  • Prof./Professor Dr. Rudy Hartono’s session moderator: also Dr. Rohani

Original video