EDU303 Technology in Foreign Language Instruction
EDU303 Technology in Foreign Language Instruction
Syllabus | International University of Sarajevo - Last Update on Oct 10, 2026
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English language and literature, Teaching
Ervin Kovačević
Course Lecturer
Course Objectives
This course prepares future language teachers to select, evaluate, design, and integrate digital technologies into foreign language instruction in pedagogically meaningful ways. It connects educational-technology principles (TPACK, SAMR) with SLA-based principles for evaluating technology use (time on task, context, input vs. intake), and applies these frameworks through hands-on, practice-intensive sessions to evaluate, design, and produce digital instructional materials. Students will also critically consider accessibility, inclusion, and the responsible/ethical use of AI-supported tools in the language classroom, learning to justify when technology adds pedagogical value and when it does not. Learning Outcomes — after successful completion of the course, the student will be able to: Explain key theoretical frameworks for technology integration in language instruction (TPACK, SAMR) and core SLA-based principles (time on task, context, input vs. intake). Select and critically evaluate digital instructional materials and courseware for foreign language teaching against pedagogical, curricular, and accessibility criteria. Design a technology-integrated lesson plan aligned with specific learning objectives and curriculum standards. Create and present digital/AI-supported learning material for a foreign language classroom, documenting design decisions and responsible AI use. Reflect critically on the ethical, practical, and inclusion-related considerations of educational technology use (digital literacy, accessibility, AI ethics) in the language classroom.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Redecker, C. (2017). European Framework for the Digital Competence of Educators: DigCompEdu. European Commission, Joint Research Centre. (Open access.) Miao, F., & Cukurova, M. (2024). AI Competency Framework for Teachers. UNESCO. (Open access.) Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054. (Freely available via author archive.) Selected instructor-provided readings, tool documentation, and case studies announced weekly.
Additional Literature
Dudeney, G., & Hockly, N. (2007). How to Teach English with Technology. Pearson Education Limited. Pegrum, M., Hockly, N., & Dudeney, G. (2023). Digital Literacies (2nd ed.). Routledge. Hockly, N. (2023). Artificial intelligence in English language teaching: The good, the bad and the ugly. RELC Journal. Mayer, R. E. (2021). Multimedia Learning (3rd ed.). Cambridge University Press. Puentedura, R. R. — SAMR Model (selected blog/presentation material, hippasus.com).Teaching Methods
Student-centered, practice-intensive delivery matching the course's 1 Teaching + 2 Practice weekly structure: Teaching (1 hour): Short lecture introducing the week's concept/framework, drawing on required reading and instructor slides
Practice I (1 hour): Instructor-led guided workshop on a real digital tool that concretely illustrates the week's concept (live where possible, screenshot-supported so a connectivity failure doesn't stall the class)
Practice II (1 hour): Guided hands-on session where students design and build something themselves — not just try the tool — producing that week's output
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Course orientation: aims, weekly structure, assessment, AI policy, classroom expectations. Student introductions. Brief first framing of "why technology in language teaching" and an initial introduction to TPACK and SAMR as the frameworks the course will use. | Course syllabus; instructor-prepared orientation materials |
| 2 📍 | TPACK and SAMR in depth: applying both frameworks to language-classroom examples. Practice I: guided Quizlet workshop. Practice II: build a small vocabulary-learning activity + SAMR justification. | Mishra & Koehler (2006, selected); Puentedura — SAMR model (selected material) |
| 3 | Pedagogy before technology: digital pedagogy, SLA-informed technology selection (DigCompEdu overview) combined with instructional design basics: aligning objectives, activities and assessment. Practice I: compare 2-3 tools for the same learning goal, screened through SLA-informed questions. Practice II: select and justify a technology for a given scenario and sketch a brief lesson storyboard. | DigCompEdu overview (Redecker, 2017); Instructor slides (background: Dudeney & Hockly, 2007, Ch. 1) |
| 4 | Multimedia learning and digital content design: cognitive load, coherence, signaling. Practice: guided redesign workshop on a poorly designed material; redesign a digital learning material. | Instructor slides (background: Mayer, 2021) |
| 5 | Interactive content and video-based learning — Quiz 1. Practice: guided interactive-video workshop; create a short video with embedded questions/activity. | Instructor slides (background: Pegrum, Hockly & Dudeney, 2023) |
| 6 | Interaction, engagement, and gamification. Practice: compare gamified tools; create an interactive language-learning activity. | DigCompEdu, Area 2-3 |
| 7 | Digital assessment and feedback. Practice: guided digital-assessment workshop; build a short formative assessment + feedback plan. | DigCompEdu, Area 4 |
| 8 | MIDTERM EXAM — applied, case-based exam: analyse a teaching problem, select and justify a technology, defend the pedagogical design in writing. | Weeks 1-7 readings |
| 9 | LMS, online, and blended/flipped learning. Practice: guided LMS module tour (institution-dependent); draft a one-week blended/online module. | Selected article; LMS walkthrough |
| 10 | Mobile-Assisted Language Learning (MALL) and learning beyond the classroom. Practice: mobile-first examples; design a phone-centered short activity. | Selected MALL reading |
| 11 | Accessibility and Universal Design for Learning (UDL). Practice: guided accessibility-feature workshop; make an earlier material accessible. | Selected UDL reading |
| 12 | Generative AI for language teaching and learning — Quiz 2. Practice: guided ChatGPT/Claude workflow; create, evaluate, and revise an AI-supported activity. | UNESCO, AI Competency Framework for Teachers (2024) |
| 13 | AI literacy, ethics, and responsible use: hallucination, bias, privacy, integrity. Practice: audit AI-generated outputs; write a short responsible-AI-use guideline. | UNESCO AI CFT (2024); Instructor slides (background: Hockly, 2023) |
| 14 | Technology-enhanced lesson design studio: integration and synthesis. Practice: final-project modelling + peer-review workshop; develop and test final project. | Project preparation |
| 15 | Student showcase and course reflection. Practice: present and peer-review; personal action plan for future teaching. | Course synthesis |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| EDU303.1 | Course | Wednesday 15:00 - 17:50 | B F2.2 | - | - |
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : LO1 LO2
Midterm Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 3
Digital Activity / Courseware Evaluation Portfolio
AI: Consult InstructorAlignment with Learning Outcomes : 2 5
Digital Material Development Project & Presentation
AI: Consult InstructorAlignment with Learning Outcomes : 2 5
Presentation
AI: Not AllowedAlignment with Learning Outcomes : 1
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Face-to-face teaching + practice
45 hours ⏳ (15 week × 3 h)
Weekly preparation and readings
20 hours ⏳ (10 week × 2 h)
Digital Activity / Courseware Evaluation Portfolio
15 hours ⏳ (1 week × 15 h)
Midterm exam preparation & applied case study
20 hours ⏳ (1 week × 20 h)
Digital Material Development Project
30 hours ⏳ (1 week × 30 h)
Quiz preparation
5 hours ⏳ (1 week × 5 h)
Final exam study
15 hours ⏳ (1 week × 15 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [EDU303] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Oct 10, 2026 | International University of Sarajevo
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| EDU303 | Technology in Foreign Language Instruction | 1 | 2 | 6 | ||||||
| Prerequisite | None | It is a prerequisite to | - | |||||||
| Lecturer | Ervin Kovačević | Office Hours / Room / Phone | Wednesday: 14:00-14:30 |
|||||||
| ekovacevic@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | This course prepares future language teachers to select, evaluate, design, and integrate digital technologies into foreign language instruction in pedagogically meaningful ways. It connects educational-technology principles (TPACK, SAMR) with SLA-based principles for evaluating technology use (time on task, context, input vs. intake), and applies these frameworks through hands-on, practice-intensive sessions to evaluate, design, and produce digital instructional materials. Students will also critically consider accessibility, inclusion, and the responsible/ethical use of AI-supported tools in the language classroom, learning to justify when technology adds pedagogical value and when it does not. Learning Outcomes — after successful completion of the course, the student will be able to: Explain key theoretical frameworks for technology integration in language instruction (TPACK, SAMR) and core SLA-based principles (time on task, context, input vs. intake). Select and critically evaluate digital instructional materials and courseware for foreign language teaching against pedagogical, curricular, and accessibility criteria. Design a technology-integrated lesson plan aligned with specific learning objectives and curriculum standards. Create and present digital/AI-supported learning material for a foreign language classroom, documenting design decisions and responsible AI use. Reflect critically on the ethical, practical, and inclusion-related considerations of educational technology use (digital literacy, accessibility, AI ethics) in the language classroom. |
|||||||||
| Textbook | Redecker, C. (2017). European Framework for the Digital Competence of Educators: DigCompEdu. European Commission, Joint Research Centre. (Open access.) Miao, F., & Cukurova, M. (2024). AI Competency Framework for Teachers. UNESCO. (Open access.) Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054. (Freely available via author archive.) Selected instructor-provided readings, tool documentation, and case studies announced weekly. | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
| Teaching Methods | Student-centered, practice-intensive delivery matching the course's 1 Teaching + 2 Practice weekly structure: Teaching (1 hour): Short lecture introducing the week's concept/framework, drawing on required reading and instructor slides. Practice I (1 hour): Instructor-led guided workshop on a real digital tool that concretely illustrates the week's concept (live where possible, screenshot-supported so a connectivity failure doesn't stall the class). Practice II (1 hour): Guided hands-on session where students design and build something themselves — not just try the tool — producing that week's output. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Course orientation: aims, weekly structure, assessment, AI policy, classroom expectations. Student introductions. Brief first framing of "why technology in language teaching" and an initial introduction to TPACK and SAMR as the frameworks the course will use. | Course syllabus; instructor-prepared orientation materials | ||||||||
| Week 2 | TPACK and SAMR in depth: applying both frameworks to language-classroom examples. Practice I: guided Quizlet workshop. Practice II: build a small vocabulary-learning activity + SAMR justification. | Mishra & Koehler (2006, selected); Puentedura — SAMR model (selected material) | ||||||||
| Week 3 | Pedagogy before technology: digital pedagogy, SLA-informed technology selection (DigCompEdu overview) combined with instructional design basics: aligning objectives, activities and assessment. Practice I: compare 2-3 tools for the same learning goal, screened through SLA-informed questions. Practice II: select and justify a technology for a given scenario and sketch a brief lesson storyboard. | DigCompEdu overview (Redecker, 2017); Instructor slides (background: Dudeney & Hockly, 2007, Ch. 1) | ||||||||
| Week 4 | Multimedia learning and digital content design: cognitive load, coherence, signaling. Practice: guided redesign workshop on a poorly designed material; redesign a digital learning material. | Instructor slides (background: Mayer, 2021) | ||||||||
| Week 5 | Interactive content and video-based learning — Quiz 1. Practice: guided interactive-video workshop; create a short video with embedded questions/activity. | Instructor slides (background: Pegrum, Hockly & Dudeney, 2023) | ||||||||
| Week 6 | Interaction, engagement, and gamification. Practice: compare gamified tools; create an interactive language-learning activity. | DigCompEdu, Area 2-3 | ||||||||
| Week 7 | Digital assessment and feedback. Practice: guided digital-assessment workshop; build a short formative assessment + feedback plan. | DigCompEdu, Area 4 | ||||||||
| Week 8 | MIDTERM EXAM — applied, case-based exam: analyse a teaching problem, select and justify a technology, defend the pedagogical design in writing. | Weeks 1-7 readings | ||||||||
| Week 9 | LMS, online, and blended/flipped learning. Practice: guided LMS module tour (institution-dependent); draft a one-week blended/online module. | Selected article; LMS walkthrough | ||||||||
| Week 10 | Mobile-Assisted Language Learning (MALL) and learning beyond the classroom. Practice: mobile-first examples; design a phone-centered short activity. | Selected MALL reading | ||||||||
| Week 11 | Accessibility and Universal Design for Learning (UDL). Practice: guided accessibility-feature workshop; make an earlier material accessible. | Selected UDL reading | ||||||||
| Week 12 | Generative AI for language teaching and learning — Quiz 2. Practice: guided ChatGPT/Claude workflow; create, evaluate, and revise an AI-supported activity. | UNESCO, AI Competency Framework for Teachers (2024) | ||||||||
| Week 13 | AI literacy, ethics, and responsible use: hallucination, bias, privacy, integrity. Practice: audit AI-generated outputs; write a short responsible-AI-use guideline. | UNESCO AI CFT (2024); Instructor slides (background: Hockly, 2023) | ||||||||
| Week 14 | Technology-enhanced lesson design studio: integration and synthesis. Practice: final-project modelling + peer-review workshop; develop and test final project. | Project preparation | ||||||||
| Week 15 | Student showcase and course reflection. Practice: present and peer-review; personal action plan for future teaching. | Course synthesis | ||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | LO1, LO2 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm Exam | 1 | 15 | 1,3 | Not Allowed | |
| Digital Activity / Courseware Evaluation Portfolio | 1 | 10 | 2,5 | Consult Instructor | |
| Digital Material Development Project & Presentation | 1 | 25 | 2,5 | Consult Instructor | |
| Presentation | 1 | 10 | 1 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Face-to-face teaching + practice | 3 | 15 | 45 | Weekly preparation and readings | 2 | 10 | 20 | |||
| Digital Activity / Courseware Evaluation Portfolio | 15 | 1 | 15 | Midterm exam preparation & applied case study | 20 | 1 | 20 | |||
| Digital Material Development Project | 30 | 1 | 30 | Quiz preparation | 5 | 1 | 5 | |||
| Final exam study | 15 | 1 | 15 | 0 | ||||||
| 0 | ||||||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 02/10/2026 | |||||||||