Emerging Technologies for Teaching and Learning

The future of education depends not on mastering technology, but on shaping the dialogue between human and artificial intelligence.
(Adapted from EDUCAUSE, 2023; Ren & Wu, 2025)

Emerging technologies for teaching and learning are changing how educators think about knowledge, design instruction, and engage students in higher education. The idea goes beyond using digital tools; it reflects an ongoing conversation between human intelligence and artificial systems that collaborate with it. Learning no longer happens only in classrooms or online spaces but across connected environments supported by data, automation, and smart technologies. At this point in reflection, the question is not about what technology can do, but how educators can keep teaching grounded in purpose and ethics so that innovation helps people rather than replaces them.

Artificial intelligence is at the center of this change, introducing adaptive feedback, personalized learning, and tools that can imitate human reasoning. The Horizon Report describes these developments as part of a larger digital transformation, where colleges and universities must build data literacy and a culture of responsible innovation to succeed (EDUCAUSE, 2023). This shift asks educators and administrators to move from occasional use of new tools to a consistent and thoughtful approach that connects policy, design, and teaching. In both education and consulting, success depends less on the technology itself and more on people being ready to use it wisely.

Big data and the Internet of Things expand this readiness by making learning observable and measurable. Smart learning environments can track participation and progress, giving educators real-time insight into how students learn (Göksel & Bozkurt, 2019). Yet data without context can lead to shallow understanding. As Eynon (2013) warned, large-scale analytics can easily prioritize efficiency and measurement over meaning, risking the loss of social and ethical context. Like in professional analytics work, meaning comes from human interpretation. Teaching with data therefore becomes a careful practice of turning patterns into understanding while protecting privacy and trust.

Artificial intelligence is also reshaping assessment. Generative tools that produce essays or code require educators to reconsider what learning outcomes truly show. Authentic assessment gains new importance, focusing on critical thinking, creativity, and ethical reasoning rather than repetition or recall (Popenici & Kerr, 2017). Instead of resisting technology, instructors can guide students to work with AI as a partner in exploration and reflection. Recent studies highlight that integrating AI into higher education calls for a mix of technological understanding, flexible teaching approaches, and ethical awareness. Faculty members express both excitement and caution, recognizing AI as a valuable aid that still depends on human guidance and judgment (Kocatas & Wu, 2024). The intelligent Technological Pedagogical Content Knowledge framework adds that readiness, innovation, and ethical reflection are key to sustainable use (Ren & Wu, 2025). The future of teaching will depend not on mastering technology, but on shaping a continuing dialogue between human and artificial intelligence that keeps learning meaningful and human centered.

References

EDUCAUSE. (2023). Horizon report: Teaching and learning edition. EDUCAUSE. https://www.educause.edu/horizon-report-teaching-and-learning-2023

Eynon, R. (2013). The rise of big data: What does it mean for education, technology, and media research? Learning, Media and Technology, 38(3), 237–240. https://doi.org/10.1080/17439884.2013.771783

Göksel, N., & Bozkurt, A. (2019). IoT and big data applications in education. Computers in Human Behavior, 93, 278–289. https://doi.org/10.1016/j.chb.2018.12.024

Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22. https://doi.org/10.1186/s41039-017-0062-8

Kocatas, O., & Wu, M. L. (2024). The role of artificial intelligence in education: Instructional technology faculty’s perspective. Journal of Ethnographic & Qualitative Research, 17(4), 283–291. (Print journal – DOI not assigned)

Ren, X., & Wu, M. L. (2025). Examining teaching competencies and challenges while integrating AI in higher education. TechTrends. https://doi.org/10.1007/s11528-025-01055-3