Modern technology in education: evidence today and questions for the future

Modern technology can change how an educational activity is prepared, accessed, completed or assessed. Those are different changes: a caption helps present spoken information in text; a drafting assistant produces material to review; a learning platform distributes work. Whether a particular use improves access, workload or understanding needs its own evidence.

This guide helps an Australian educator turn a broad proposal into a specific question. It is not a census of technology use, a recommended-products list or a forecast that every classroom will become AI-led. For earlier changes, see the Australian education technology timeline; for classroom harms and privacy questions, see the education technology risk guide.

What current guidance and a measured trial actually establish

Sources checked 15 September 2026. This is a deliberately small, mixed evidence set, not a systematic review. The three Australian rows describe responsibilities or policy; the English trial measures a particular workload outcome. They cannot be averaged into a score for “technology in education”.

The source table scrolls horizontally on small screens. Focus it to use the arrow keys.

A promising result, with the denominator left attached

The EEF project summary reports 259 teachers in 68 English secondary schools. Preparation time averaged 56.2 minutes per week in the ChatGPT group versus 81.5 in the comparison: 25.3 minutes less, approximately 31%. That concerns the selected science preparation, not the entire working week.

The linked December 2024 evaluation report, executive summary, PDF file pages 5–6, describes weekly diaries and an outcome window in weeks 6–10 after five initial weeks. It reports incomplete diary data excluded 18.5% of enrolled teachers from the primary analysis. A blinded resource review found no evidence of a quality difference; that is not proof of identical quality or improved student attainment.

Before transferring a result, compare subject, staff experience, approved tools, preparation task and support. A faster first draft can still require substantial checking. Keep setup, verification and correction in a workload estimate rather than reporting generation time alone.

Worked example: a caption exists, but does it preserve the explanation?

Invented editorial fixture—not a recording, product test or learner study: a teacher wants text access to a short explanation. The original script below is written for this example. The two faulty captions are deliberately constructed to show what a review must catch.

Original script: “This activity is optional. Do not submit it for a grade. The water sample starts at twenty degrees Celsius.”

1. Faulty caption A: “This activity is optional. Submit it for a grade. The water sample starts at twenty degrees Celsius.” Removing the negation reverses the instruction. The caption fails even though the surrounding words are correct.
2. Faulty caption B: “This activity is optional. Do not submit it for a grade. The water sample starts at twenty degrees Fahrenheit.” The unit is wrong. A spelling check would not establish that the scientific meaning was preserved.
3. Reviewed text: the original script above is the reference for these fixtures. Restoring its words resolves these two errors; it does not test timing, speaker identification, player controls or whether learners can use the eventual resource.

A useful proposal is therefore “provide accurate, usable text for this explanation”, not “enable AI captions and claim inclusion”. In a real recording, compare with the actual speech; a script alone may differ from what was said. Do not use student recordings or identifiable material for a tool demonstration without the required institutional approval.

A completed proposal—and a record you can reuse

Task and audience

Fictional staff-only review of one teacher-authored explanation. No learner account, voice recording or student work is used.

Current alternative

Publish the teacher’s reviewed text alongside the explanation using an existing approved system.

Proposed change

Investigate whether assisted caption preparation would improve that process. The invented fixtures above are only a first content check.

Evidence still missing

Actual audio alignment, keyboard and assistive-technology usability, learner needs, total preparation effort and the institution’s tool decision.

Decision now

Keep the approved text route. Do not begin a student pilot or claim improved access from these fixtures. Take the unanswered questions to the institution’s teaching, accessibility and privacy teams.

Copy the following into your planning notes. It is a proposal worksheet, not an approved research protocol or a promise of a downloadable classroom kit.

Specific reader / learner group and educational task:
Problem with the current approach, with existing evidence:
Current non-AI or already-approved alternative:
Proposed change and why it might help:
Supporting source / setting / date / measured outcome:
Important differences between that evidence and this setting:
Tool, account, input material, rights and data destination:
Required institutional decisions and responsible people:
Quality and accessibility checks / serious-error stop condition:
Comparison, outcome definition and missing-data handling:
Setup, review, correction and ongoing workload to count:
Proposed duration and reason / permissions still required:
Decision: keep current approach, investigate, pause or seek approval:
Next evidence, owner and review date:

Future possibilities: replace a prediction with a testable question

• More tailored feedback: can learners explain the next problem without the assistant, and what happens when its advice is wrong? A polished answer is not evidence of retained understanding.
• More simulation: which parts of a task can be rehearsed, and which still require physical performance, supervision or accredited assessment? A simulation should not be assumed to replace a placement.
• More connected learning records: who accepts the record, under which rules, and can the learner retrieve it in a usable format? Technical portability is different from credit recognition.

These are possible directions, not a prediction that they will arrive in three to five years. A future claim becomes useful when it names a decision, a constraint and an observation that could disprove it. The higher-education assessment and credit guide works through two of those decisions for students.

Resolve approval before collecting information

OAIC’s education guidance distinguishes institutions: private education is usually covered by the federal Privacy Act; public schools, TAFEs and most universities instead may fall under state or territory privacy laws. ANU and private universities are federal Act exceptions to that general tertiary distinction. Ask the responsible institutional team which obligations apply.

An Australian storage location, a consent box or a staff-only demonstration is not a complete approval process. Establish what material may be used, who decides, and what safeguards and alternatives are required. This article does not authorise data collection, assessment changes or procurement.

The source comparison, caption fixtures and worksheet are MLAI editorial work, not new research or independent specialist approval. There is no school-consulting or contractor offer attached to this education resource.

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About the Author

Dr Sam Donegan

Medical Doctor, AI Startup Founder & Lead Editor

Sam leads the MLAI editorial team, combining deep research in machine learning with practical guidance for Australian teams adopting AI responsibly.