UsesLevel 4 of 6 occupation or activityOccupation or activity, level 4 of 6Used to calculate 8 entries, levels 5 and 6Calculated with 12 entries, levels 1 and 3Database structure 
6Territories
5Organisations
3Uses
2Machine operations
1Calculation factorsTeacher, 1 year
A year of work for a secondary school teacher who uses AI: about 53 lessons prepared, 170 quick questions, 4.4 presentations of 10 slides, and 8 other tasks. Projet Celsius estimate.
- Text and conversation account for 98% of emissions: "Prepare a lesson" weighs as much as 21 quick questions.
- About 430 requests to AI over the year, or 2 per working day.
- 2.3 times lower than the AI footprint of an office employee, for the same use.
- An estimate: the frequencies and the share done with AI are our assumptions.
Sentence to citeTeacher, 1 year, Projet Celsius estimate: 260 gCO2e per position per year, for 570 Wh of electricity, servers worldwide (458 gCO2e/kWh).
Breakdown of emissions by item
260 gCO2e in total: 570 Wh of electricity × 458 gCO2e/kWh (servers worldwide).
Inputs of the calculation
PUE of the data centre, Read 1,000 tokens, Re-read from cache, Write, with reasoning, Write 1,000 tokens, Generate an image, Prepare a lesson, Quick question, Presentation of 10 slides
How the calculation is built
A year of work for a secondary school teacher who uses AI, task by task: lessons prepared, exercise sheets, marked papers, comments on pupils' work, emails. The frequencies come from teachers' working time (DEPP, 2025) and O*NET, AI use from the Gallup survey (2025); these are assumptions, and they can be adjusted.
Comparison with other occupations and activities
Characteristics
| Unit | 1 person, 1 year : 220 days |
|---|---|
| Default location | world, 458 gCO2e/kWh (Ember 2025, life cycle); 11 other locations to choose from |
| Counted | the tasks done with AI over a working year, including data centre cooling |
| Not counted | server manufacturing, model training, the person's device and the tasks done without AI |
| Status | estimated |
All the characteristics
| Identifier | IA-0316, projetcelsius.com/en/ai/database/IA-0316/ |
|---|---|
| Version | 0.1 of 2 November 2026 |
| Category | Occupations and activities, one person, one year |
| Choice of the selected value | database calculation from the entries this one is made of, at the quantities of the unit |
| Sources | calculation of the database |
| Licence | CC BY 4.0 |
Usage parameters
Advanced settings
Tasks and frequencies
Frequency and share done with AI, task by task · typical use
Adjust each task
| Task | Frequency | Share with AI | Per year | Electricity | Share |
|---|---|---|---|---|---|
| Prepare a lesson | 52.8 | 330 Wh | 58 % | ||
| Write a page | 26.4 | 12 Wh | 2 % | ||
| Proofread a page | 52.8 | 32 Wh | 6 % | ||
| Quick question | 170 | 51 Wh | 9 % | ||
| Write an email | 88 | 20 Wh | 4 % | ||
| Create an image | 4.4 | 14 Wh | 2 % | ||
| Presentation of 10 slides | 4.4 | 50 Wh | 9 % | ||
| Conversation of 10 exchanges | 7.48 | 26 Wh | 5 % | ||
| Write meeting minutes | 11 | 16 Wh | 3 % | ||
| Summarise 30 pages | 2.5 | 12 Wh | 2 % | ||
| Translate a page | 11 | 6.6 Wh | 1 % |
Result for the chosen case
220 days, servers worldwide: 260 gCO2e, 570 Wh.
For a carbon footprint
| Item | purchases of services (GHG Protocol scope 3, category 1); regulatory BEGES, category 4 |
|---|---|
| Quantity | 1 role over one year |
| Factor | 260 gCO2e per position over a year, IA-0316 version 0.1, based on the local grid (location-based), servers worldwide |
| Total | 260 gCO2e · not counted: server manufacturing, model training, the person's device and the tasks done without AI |
Typical usage profile
Projet Celsius assumption
Over 18 hours of teaching, the typical teacher prepares a little more than one session with AI, makes an exercise sheet every 2 weeks, asks it 4 questions or comments and has it write 2 emails; they mark by hand, with only one paper in 50 going through AI. About 9.8 requests a week. Over the year, about 570 Wh, of which 58% for the lessons prepared: for them, AI helps with preparation and produces almost nothing of what is delivered. Light use, 130 Wh; heavy use, 2.4 kWh, when 5 sessions and 9 papers a week go through AI.
Breakdown by item
Quantity, unit value, contribution and nature of the data for each item
| Item | Quantity | Unit value | Contribution | Share | Nature of the data |
|---|---|---|---|---|---|
| Prepare a lesson | 53 times | 6.2 Wh | 330 Wh | 58 % | assumption |
| Quick question | 170 times | 0.3 Wh | 51 Wh | 9 % | assumption |
| Presentation of 10 slides | 4.4 times | 11 Wh | 50 Wh | 9 % | assumption |
| Proofread a page | 53 times | 0.6 Wh | 32 Wh | 6 % | assumption |
| Conversation of 10 exchanges | 7.5 times | 3.5 Wh | 26 Wh | 5 % | assumption |
| Write an email | 88 times | 0.23 Wh | 20 Wh | 4 % | assumption |
| Write meeting minutes | 11 times | 1.4 Wh | 16 Wh | 3 % | assumption |
| Create an image | 4.4 times | 3.2 Wh | 14 Wh | 2 % | assumption |
| Summarise 30 pages | 2.5 times | 4.9 Wh | 12 Wh | 2 % | assumption |
| Write a page | 26 times | 0.45 Wh | 12 Wh | 2 % | assumption |
| Translate a page | 11 times | 0.6 Wh | 6.6 Wh | 1 % | assumption |
Sources of the calculation
16 publications, for this entry and those used to calculate it, with the chosen parameters.
The result is calculated by Projet Celsius. The sources below support its uses, its factors and its conversion; they do not all measure this total.
Uses and assumptions
These publications shed light on AI tasks and uses. They do not measure the carbon footprint of the case calculated.
Anthropic · February 2025
Role and limits : Anthropic
Author. Kunal Handa et al. (Anthropic)
What the publication covers. Claude.ai conversations matched to O*NET tasks and occupations
What it does not cover. API use and enterprise plans
Reading by the database. A single assistant, more oriented towards code than average
Published observation on uses
Scope of the figure used. Share of conversations related to education and libraries
Limit. Figure read from the illustration on the publisher's page
Apec · June 2025
Role and limits : Apec
What the publication covers. frequency of generative AI use at work, 2,000 private-sector managers surveyed in March 2025
What it does not cover. non-managers and the number of requests
Reading by the database. Recent French survey on frequency of use at work; copy read on a specialist press site
Support for the "Typical use" setting
Scope of the figure used. The median user among those who use AI, end of 2026: they turn to it every working day for part of their questions, writing and reading, about 5 to 15 requests per day in an office job. In an organisation or a territory, it also sets the share of employees who use it (25%).
Limit. This setting remains an assumption of the database, not a measurement of the occupation.
Crédoc · 2026
Role and limits : Crédoc
Author. Crédoc for Arcep, Arcom, the CGE and the ANCT
What the publication covers. use of generative AI by people aged 12 and over in France in 2025: who, for what, how often, paid or not
What it does not cover. the number of requests
Reading by the database. The reference survey in France, 4,145 people
Teacher, 1 year → : Quick question →
About 660 questions and 360 report comments per year, assumption; share done with AI based on the Crédoc Baromètre du numérique (edition 2026, data 2025), adjusted to the occupation.
Scope of the figure used. computation of the response, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
DEPP · November 2025
Role and limits : DEPP
Author. DEPP, ministère de l'Éducation nationale
What the publication covers. working time reported over a typical week: teaching, lesson preparation, marking, other activities
What it does not cover. the use of digital tools and AI
Reading by the database. French public reference source on teachers' time
Published observation on uses
Scope of the figure used. Hours of classroom teaching reported by secondary school teachers, for 18 hours of service for certified teachers
Limit. Supports the frequency of lesson preparation
Published observation on uses
Scope of the figure used. Share of time spent preparing lessons among certified teachers, for about 42 hours of work a week
Limit. Supports the frequency of lesson preparation
EnronSent
Role and limits : EnronSent
Author. Will Styler
What the publication covers. 96,106 messages sent by Enron employees, 13,810,266 words
What it does not cover. the messages received
Reading by the database. Professional emails from 2000 and 2001: an order of magnitude, not a norm
Write an email → : Write, with reasoning →
The email written, 150 words
Scope of the figure used. server, excluding the data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Write an email → : Read 1,000 tokens →
Length convention: an email
Scope of the figure used. A typical email: the sent messages of the Enron corpus average about 144 words; figure read in an excerpt, notice not verified
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Write an email → : Write, with reasoning →
Length convention: an email
Scope of the figure used. A typical email: the sent messages of the Enron corpus average about 144 words; figure read in an excerpt, notice not verified
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
The 5 other publications in this section
Epoch AI · February 2025
Role and limits
What the publication covers. the calculation from the hardware and the assumed size of the model
What it does not cover. actual measurements
Reading by the database. A public, transparent calculation
Conversation of 10 exchanges → : Re-read from cache →
The context re-read from cache at each turn: the previous exchanges, and the document already in the conversation if there is one
Scope of the figure used. server, convention C4
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Reference of the selected value
Scope of the figure used. GPT-4o, 500 tokens written, full scope
Limit. A public calculation, from the hardware and the assumed size of the model
Gallup · June 2025
Teaching for Tomorrow: Unlocking Six Weeks a Year With AI ↗ (new tab)
Role and limits
Author. Gallup and Walton Family Foundation
What the publication covers. use of AI by American primary and secondary school teachers, by task and frequency, year 2024-2025
What it does not cover. teachers outside the United States
Reading by the database. American survey; France would be lower (OECD TALIS survey, not reviewed)
Published observation on uses
Scope of the figure used. American primary and secondary school teachers who used an AI tool for their work in 2024-2025
Limit. Supports the teacher's use of AI; American survey, France is lower
Published observation on uses
Scope of the figure used. Teachers who prepare their lessons with AI at least once a month (about 17% every week, 3% every day)
Limit. Supports the teacher's use of AI; American survey, France is lower
Published observation on uses
Scope of the figure used. Teachers who make their exercise sheets with AI at least once a month
Limit. Supports the teacher's use of AI; American survey, France is lower
Published observation on uses
Scope of the figure used. Teachers who mark or comment on pupils' papers with AI at least once a month
Limit. Supports the teacher's use of AI; American survey, France is lower
Published observation on uses
Scope of the figure used. Time saved reported by teachers who use AI every week
Limit. Self-reported; not used in the calculation
Teacher, 1 year → : Prepare a lesson →
About 8 lessons prepared or redone per week, for 18 hours of teaching and a quarter of working time spent on preparation (DEPP, 2025); share done with AI based on the 37% of teachers who prepare their lessons with AI at least once a month (Gallup, 2025).
Scope of the figure used. simple tasks, data centre included
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Teacher, 1 year → : Write a page →
About 4 exercise sheets or assessments per week, assumption; share done with AI based on the 33% who make their sheets with AI at least once a month (Gallup, 2025).
Scope of the figure used. tokens read and written, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Teacher, 1 year → : Proofread a page →
About 60 marked papers per week, marking being weekly or more often in O*NET; very low share done with AI, only 16% marking with AI at least once a month (Gallup, 2025).
Scope of the figure used. tokens read and written, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Teacher, 1 year → : 10-slide presentation →
About 1 slide deck per week, assumption; share done with AI based on Gallup (2025).
Scope of the figure used. simple tasks, data centre included
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Microsoft Research · July 2025
Working with AI: Measuring the Applicability of Generative AI to Occupations ↗ (new tab)
Role and limits
Author. Kiran Tomlinson, Sonia Jaffe, Will Wang, Scott Counts, Siddharth Suri (Microsoft)
What the publication covers. 200,000 conversations with Bing Copilot matched to O*NET activities; applicability score by occupation, in open data (version 7 of October 2026)
What it does not cover. actual adoption by occupation
Reading by the database. Measures what AI is used for and succeeds at, not who uses it
Published observation on uses
Scope of the figure used. Generative AI applicability score for secondary school teachers (code 25-2031), 200,000 conversations with Bing Copilot, from 0 to 1
Limit. Used for the occupation coefficient: square root of the score divided by 0.25, the score of office employees
Teacher, 1 year → : Write an email →
About 5 emails per day (families, colleagues), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Scope of the figure used. tokens read and written, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Teacher, 1 year → : Write meeting minutes →
About 1 report per week (class council, meeting), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Scope of the figure used. simple tasks, data centre included
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Teacher, 1 year → : Summarise 30 pages →
About 10 long texts to read per year (curriculum, report), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Scope of the figure used. document read, summary written, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Teacher, 1 year → : Translate a page →
About 1 page to translate every 2 weeks, assumption; high share done with AI, translation being among the tasks most often handed to AI (Microsoft, 2025).
Scope of the figure used. tokens read and written, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Role and limits
Author. National Center for O*NET Development, US Department of Labor
What the publication covers. tasks of each occupation and frequency reported by job holders, in 7 classes from "once a year or less" to "every hour or more"
What it does not cover. occupations without a frequency survey, such as data scientists
Reading by the database. The reference classification of tasks; US job holders
Published observation on uses
Scope of the figure used. secondary school teachers: teaching (lessons, discussions, demonstrations), share of permanent staff who do so daily or more often
Limit. Support for the frequency of the task in the basket; US job holders
Published observation on uses
Scope of the figure used. secondary school teachers: preparing, administering and marking assessments, share who do so daily or more often (78% weekly or more often)
Limit. Support for the frequency of the task in the basket; US job holders
Published observation on uses
Scope of the figure used. secondary school teachers: setting and marking exercises and homework, share who do so daily or more often
Limit. Support for the frequency of the task in the basket; US job holders
Teacher, 1 year → : Conversation of 10 exchanges →
About 1 sequence to adapt per week, adapting to pupils' needs being daily in O*NET; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Scope of the figure used. messages, context re-read from cache, responses, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Projet Celsius · November 2026
Tokens per word, per line of code and per cell, measured with 7 public tokenisers
Role and limits
What the publication covers. 479,888 words from 196 blog articles and their English translation, code from the site and the Python library, a table of measurements; tokenisers of GPT-4o, GPT-4, Llama 3, Gemini (via Gemma 3), Mistral, DeepSeek and Qwen
What it does not cover. the Claude tokeniser, not public
Reading by the database. A measurement on technical texts, figures included; to be repeated if publishers change their tokeniser
Projet Celsius measurement, repeated on request with the public tokenisers
Conversation of 10 exchanges → : Re-read from cache →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Write a page → : Write, with reasoning →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Proofread a page → : Read 1,000 tokens →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Proofread a page → : Write, with reasoning →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Summarise 30 pages → : Read 1,000 tokens →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Summarise 30 pages → : Write, with reasoning →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Translate a page → : Write, with reasoning →
The translated page, counted as a French page
Scope of the figure used. server, excluding the data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Translate a page → : Read 1,000 tokens →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Translate a page → : Write, with reasoning →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Energy and factors
The references of the selected values and of the entries used in this calculation.
Role and limits : Google
What the publication covers. chips, host, standby machines, data centre, on-site water
What it does not cover. manufacturing, training
Reading by the database. The only production measurement published by a provider
Reference of the selected value
Scope of the figure used. Median of a text prompt; chips, host, standby machines, data centre
Limit. The only production measurement published by a provider, not audited by a third party
Luccioni et al. · October 2024
Role and limits : Luccioni et al.
Author. Luccioni, Jernite and Strubell
What the publication covers. GPU, processor, memory
What it does not cover. data centre
Reading by the database. Open models only
Create an image → : Generate an image →
One image, of the chosen size: standard by default
Scope of the figure used. GPU, processor, memory; excluding the data centre
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Reference for the variant "Standard image, from 512 to 1,024 pixels per side"
Scope of the figure used. Average of the open models tested, 1,000 images; median 1.35 Wh, maximum 11.49 Wh
Limit. Open models only; closed generators publish nothing
Microsoft Research · 2026
Estimation de l'énergie des requêtes en production (Joule) ↗ (new tab)
Role and limits : Microsoft Research
Author. Microsoft Research, Oviedo et al.
What the publication covers. H100 nodes, production
Reading by the database. Calibrated on production
Reference of the selected value
Scope of the figure used. Median, 500 tokens read and 300 written, H100 nodes
Limit. Not a measurement, but calibrated on production; judges public estimates to be 4 to 20 times too high
OpenAI · June 2025
Role and limits : OpenAI
Author. Sam Altman, OpenAI
What the publication covers. an average query
What it does not cover. method, scope
Reading by the database. One sentence, no method
Reference of the selected value
Scope of the figure used. "An average query", with no published method
Limit. No published scope; consistent with Google
Watershed · Aug. 2026
Estimating GHG Emissions from AI Use: Framework for Corporate-Level Measurement ↗ (new tab)
Role and limits : Watershed
Author. Bistline, Ulissi, Davis, Glidden, Joyce, Li, Mohsenin and Suh (Watershed)
What the publication covers. factors per 1,000 tokens read and written at the server before PUE (host and standby machines included), PUE separate (1.10 for a large operator, 1.56 for an unknown operator)
What it does not cover. clean measurements, the model and the hardware
Reading by the database. Default factors proposed for review (public data as of June 2026); for a large operator, taken from Google's measurement published in 2025 (Elsworth et al.), which already contains the data centre overhead; constant per token, they underestimate long contexts and agents according to the authors
Reference for the variant "Medium model, Watershed default factor"
Scope of the figure used. Default factor for 1,000 tokens read, medium model, large operator, at the server before PUE (Watershed table 2, public data as of June 2026)
Limit. A default value that Watershed derives from Google's measurement (0.24 Wh for the median text query of the Gemini app), with about 500 tokens per query and a written token costing 3 times a read token; Watershed does not give the split between read and written tokens. The 0.24 Wh already include the overhead of Google's data centre (0.02 Wh, 8%): with 375 tokens read and 125 written, the simplest reading, the factor contains it, and the PUE of 1.10 applied afterwards counts it a second time (about 9%). Values proposed for review, final at the next version of the framework
Reference for the variant "Medium model, Watershed default factor"
Scope of the figure used. Default factor for 1,000 tokens written, medium model, large operator, at the server before PUE (Watershed table 2, public data as of June 2026)
Limit. 3 times the reading factor, based on the ratio between writing and reading selected by Watershed (Patel et al., Splitwise, 2024). Same derivation and same caveat as for reading: Google's 0.24 Wh already include about 8% of data centre. Values proposed for review, final at the next version of the framework
Conversation of 10 exchanges → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Reference of the selected value
Scope of the figure used. Default factor for a large operator, from the server to the data centre; 1.56 for an unknown operator
Limit. A default value, not a measurement
Article of 1,000 words → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Write a page → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Proofread a page → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Quick question → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Write an email → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Create an image → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Summarise 30 pages → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Translate a page → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Conversion into carbon
The source of the electricity factor of the selected location.
Worldwide: 458 gCO2e/kWh, life cycle.
Role and limits : Ember
Author. Ember, Our World in Data
What the publication covers. electricity generation over the life cycle, by country
What it does not cover. guarantees of origin
Reading by the database. The same source for all countries
Conversion of electricity into carbon: worldwide
Scope of the figure used. the production of electricity over the life cycle, including power plant construction and fuels
Limit. An average for the chosen country, not the electricity contract or the data centre of a specific service.
Nature of the data
Published readings, conventions, assumptions
- Projet Celsius assumptions 100 %
Usage assumptions
Frequency, share done with AI according to adoption, source of each assumption
Prepare a lesson : 8 per week, 15% with AI in typical use
About 8 lessons prepared or redone per week, for 18 hours of teaching and a quarter of working time spent on preparation (DEPP, 2025); share done with AI based on the 37% of teachers who prepare their lessons with AI at least once a month (Gallup, 2025).
Write a page : 4 per week, 15% with AI in typical use
About 4 exercise sheets or assessments per week, assumption; share done with AI based on the 33% who make their sheets with AI at least once a month (Gallup, 2025).
Proofread a page : 60 per week, 2% with AI in typical use
About 60 marked papers per week, marking being weekly or more often in O*NET; very low share done with AI, only 16% marking with AI at least once a month (Gallup, 2025).
Quick question : 1,000 per year, 17% with AI in typical use
About 660 questions and 360 report comments per year, assumption; share done with AI based on the Crédoc Baromètre du numérique (edition 2026, data 2025), adjusted to the occupation.
Write an email : 5 per day, 8% with AI in typical use
About 5 emails per day (families, colleagues), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Create an image : 2 per week, 5% with AI in typical use
About 2 lesson illustrations per week, assumption; low share done with AI, assumption.
Presentation of 10 slides : 1 per week, 10% with AI in typical use
About 1 slide deck per week, assumption; share done with AI based on Gallup (2025).
Conversation of 10 exchanges : 1 per week, 17% with AI in typical use
About 1 sequence to adapt per week, adapting to pupils' needs being daily in O*NET; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Write meeting minutes : 1 per week, 25% with AI in typical use
About 1 report per week (class council, meeting), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Summarise 30 pages : 10 per year, 25% with AI in typical use
About 10 long texts to read per year (curriculum, report), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Translate a page : 0.5 per week, 50% with AI in typical use
About 1 page to translate every 2 weeks, assumption; high share done with AI, translation being among the tasks most often handed to AI (Microsoft, 2025).
Published figures in support
Anthropic (2025): 9.3% of conversations
Share of conversations related to education and libraries
Microsoft Research, Tomlinson et al. (2026): 0.176 applicability score
Generative AI applicability score for secondary school teachers (code 25-2031), 200,000 conversations with Bing Copilot, from 0 to 1
O*NET (2025): 88% of job holders
secondary school teachers: teaching (lessons, discussions, demonstrations), share of permanent staff who do so daily or more often
O*NET (2025): 38% of job holders
secondary school teachers: preparing, administering and marking assessments, share who do so daily or more often (78% weekly or more often)
O*NET (2025): 74% of job holders
secondary school teachers: setting and marking exercises and homework, share who do so daily or more often
Gallup and Walton Family Foundation (2025): 60% of teachers
American primary and secondary school teachers who used an AI tool for their work in 2024-2025
Gallup and Walton Family Foundation (2025): 37% of teachers
Teachers who prepare their lessons with AI at least once a month (about 17% every week, 3% every day)
Gallup and Walton Family Foundation (2025): 33% of teachers
Teachers who make their exercise sheets with AI at least once a month
Gallup and Walton Family Foundation (2025): 16% of teachers
Teachers who mark or comment on pupils' papers with AI at least once a month
Gallup and Walton Family Foundation (2025): 5.9 hours a week
Time saved reported by teachers who use AI every week
DEPP (2025): 19.4 hours a week
Hours of classroom teaching reported by secondary school teachers, for 18 hours of service for certified teachers
DEPP (2025): 23% of working time
Share of time spent preparing lessons among certified teachers, for about 42 hours of work a week
Methodology conventions
With their alternatives
| The working day and year | A working year counts 220 days and 44 weeks, a personal year 365 days; the task lists of occupations give, as a median, about 2 requests to AI per working day at low adoption, 6 at standard adoption, 21 at intensive adoption, excluding code suggestions |
|---|---|
| The default location | Without other information, the servers are in the world, at the world average electricity |
| Electricity, location-based, on a life cycle basis | The kilowatt-hour is that of the servers' country, on a life cycle basis, from a single source for all countries |
| Two significant figures | A calculated value is rounded to two significant figures, a published value is reproduced with the precision of its source; the calculation keeps full precision |
| The formula | the sum of what makes up the entry, then CO2e: electricity (Wh) multiplied by the carbon intensity of the server location (gCO2e/kWh), divided by 1,000 |
| Calculate a case without an entry | The formula, the default values and a step-by-step example |
History
Entry in version 0.1 of 2 November 2026, in the database in version 0.4
- 2 November 2026Version 0.1 of the entry, as of the date of its current value.
- 31 October 2026Creation of the database, in its first version (0.1).
