How technology can affect education negatively

Technology can create problems in education when a task becomes harder to attend to, cannot be accessed by some learners, exposes information, or produces answers that nobody checks. A tool can also appear to save time while moving work to students, families or other staff. These are different failure modes: identify the one you are concerned about before deciding what to change.

Use this guide to prepare a conversation about one activity with the appropriate teacher, school leader or support team. It is not a lesson prescription, a health assessment or authority to trial a new service with children. For the historical question rather than a risk decision, see how technology has changed Australian education.

Download the printable teacher/parent discussion checklist and risk record. For benefits and future proposals alongside these risks, read the balanced education technology overview.

What the distraction evidence actually supports

In PISA 2022 Results, Volume II, chapter 3, OECD reports that about one in three students across OECD countries said they were distracted by using digital devices in most or every mathematics lesson. About one in four reported distraction from other students’ devices. The chapter also reports an association between distraction and mathematics performance.

Those are student reports and statistical associations, not an experiment proving that devices caused lower scores. They are not measurements of this school, every subject or every age group. This source does not validate a universal lesson length, screen ban or expected improvement. The 2022 data were reported in 2023, not newly collected in 2026.

Before repeating a learning claim, identify its setting, participants, comparison, outcome and uncertainty. “More work submitted”, “students liked it” and “students could explain the idea without assistance” measure different things. Removing a distraction does not by itself establish that learning improved.

Turn a broad concern into something you can examine

The following is an editorial discussion aid, not a validated risk scale. Ask learners and staff about barriers through the school’s approved channels; do not introduce monitoring or collect sensitive information simply to fill it in.

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A non-digital alternative is not a failed digital lesson

The NSW education department’s Rowena Public School snapshot, originally published 14 July 2020, describes device loans, printed learning packs and recorded lessons in a community with unreliable internet. It is a documented response to that school’s circumstances, not a controlled comparison establishing the best approach everywhere.

An equivalent alternative needs to preserve the learning opportunity, not just supply a different file. Ask how someone will receive instructions, complete the task, obtain feedback and ask for help. Do not remove an accessibility tool merely because the activity is described as “screen free”.

Correct the privacy question before choosing controls

OAIC’s education guidance says private schools are usually covered by the federal Privacy Act. Public schools are not covered in the same way; state or territory privacy laws may apply instead. The previous version’s blanket federal-law framing was too broad. The applicable sector, jurisdiction, service and data use matter.

Ask the school or sector privacy officer which approval process applies, what data are necessary, how vendor use and retention are assessed, and who handles access or incident concerns. Single sign-on and a data-processing agreement can be parts of a review; neither makes a tool compliant or suitable by itself. Do not treat a vendor’s “education” label as approval.

The Australian school generative-AI framework resource page records ministerial endorsement of its 2024 review in June 2025. That is national guidance, not permission to use any particular product or exemption from school rules. Check the applicable local policy and authorised advice before proposing a student-facing use.

A fictional decision that does not hide the stop condition

Invented staff-planning example—not a classroom trial: a teacher considers an AI service to draft hints for a fractions exercise. The intended task is for learners to explain their reasoning. Staff first review three made-up questions without creating student accounts or uploading student work.

These are complete editorial review fixtures, not observed output from an AI product:

1. Question: what is 1/2 + 1/4? A correct explanation converts 1/2 to 2/4, then adds 2/4 + 1/4 = 3/4. The deliberately incorrect fictional hint says “The answer is 3/4: add the top numbers and the bottom numbers.” That rule would instead produce 2/6; the final answer happens to be right but the explanation must be rejected.
2. Question: which is larger, 2/3 or 3/4? Compare 8/12 with 9/12: 3/4 is larger. A review should check the reasoning, not merely whether the larger fraction was selected.
3. Incomplete question: what is half of this? No quantity is supplied. The expected response asks which quantity is meant rather than inventing a whole or an answer.

• Baseline: preparing the existing hint sheet is assumed to take 30 staff-minutes.
• Drafting route: 10 minutes preparing inputs + 8 generating drafts + 18 checking and correcting = 36 staff-minutes. That is six minutes more, not a saving; licences, training and other people’s time are excluded.
• Concern found: one draft gives the correct final answer with an invalid explanation. Output completion is therefore not a sufficient acceptance check.
• Access and data: keep the existing accessible format available. The proposed student-account route has no documented approval.
• Decision: do not proceed to student use. The teacher retains the existing approach and asks the authorised school lead whether any further staff-only assessment is warranted.

The numbers and error are fictional. They illustrate recording full effort and a reason to stop, not the typical accuracy, cost or educational effect of AI. Any later classroom evaluation would need the school’s authorised design, appropriate safeguards and a suitable learning measure. A short before/after comparison alone would not isolate the cause of a change.

Copy the classroom technology risk record

Use this text in a document you control. It replaces the generic download card; the page does not collect responses. Keep real student records in the school’s approved systems, not in public examples or an unapproved AI service.

Activity / learners and setting / date:
Intended learning task and existing approach:
Specific risk and who could be affected:
Source or observation / date / what it cannot establish:
Baseline: learning evidence, access and separate effort/cost measures:
Proposed change and an equivalent accessible alternative:
Data needed / destination / unresolved approval questions:
Potential mitigation / evidence it has actually been implemented:
Observable harm or failure signal / condition for pausing:
Responsible person and authorised decision-maker:
Decision / unresolved issues / next review date:
What can be shared safely, with whose permission:

A parent’s concrete discussion question

Fictional example: a learner cannot reliably open a homework service at home. The parent can describe the task and access problem without sharing a diagnosis or other private details: “What equivalent format can we use, how will feedback reach us, and who should we contact if that also fails?” Record the agreed route and review date; do not infer the learner lacks motivation or buy a subscription before the school clarifies the requirement.

Use the record with the people responsible for the decision

Take one concrete question and the missing evidence to the relevant school team. MLAI does not offer an education-compliance assessment through this article. A general community discussion cannot approve a classroom tool, and no recruitment or implementation offer is implied.

Sources were checked on 9 September 2026. This is a scoped evidence-reading guide with an original fictional exercise, not a systematic review, an independently reviewed school procedure or a claim of measured MLAI educational outcomes. It makes no medical claim about sleep, mood or a safe amount of screen time.

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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.