AI uses in education (to be completed)

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AI uses in education (to be completed) da Mind Map: AI uses in education (to be completed)

1. Definition (link)

2. Teach AI! (link)

2.1. Big data

2.2. articulation Dataviz articulation

2.3. Discovery of the principles

2.4. Issues, hopes and fears

2.4.1. EMI

2.4.2. Digital culture

2.4.3. foresight scenarios

2.5. Discovery and treatment of biases

2.5.1. MachineLearningForKids

2.6. chatbot programming

2.6.1. Complexity

2.6.1.1. Simple decision tree

2.6.1.2. Request by call for services

2.6.2. usable tools

2.6.2.1. SAP (ex recast.ai)

2.6.2.1.1. GitHub account

2.6.2.1.2. Free Prototype

2.6.2.2. MachineLearningForKids (link)

2.7. Deep learning

2.7.1. programming Tensorflow.js type

2.7.2. example on

3. adaptive learning adaptation

3.1. of activities

3.1.1. choice of the following activity

3.1.2. course over several sessions

3.1.3. research balance

3.1.3.1. challenge

3.1.3.1.1. sufficiently motivating

3.1.3.2. probable success

3.1.4. examples

3.1.4.1. Kwyk

3.1.4.1.1. math exercises

3.1.4.2. Lalilo

3.1.4.2.1. learning grapheme / phoneme

3.1.4.3. Glossary

3.1.4.3.1. adaptive reader adaptation

3.2. of content

3.2.1. by recommendation

3.3. memory anchoring

3.3.1. identification of the forgetting cycle

3.3.2. adaptation of the recall rhythm

3.3.3. examples

3.3.3.1. Wonoz

3.3.3.2. Orthodidacte

3.3.3.3. Bescherelles

3.3.3.4. Frantastique

4. adaptive evalutation

4.1. evaluation

4.1.1. predictive

4.1.2. formative

4.1.2.1. avoid acquired impotence (link)

4.1.3. n / a mmative

4.2. continuous evaluation

4.2.1. from activities

4.2.2. without summative evaluation

4.2.3. shortening trees

4.2.3.1. Pix

4.3. feedback

4.3.1. automated

4.3.1.1. offered to students

4.3.1.2. coaching link student

4.3.2. semi automated

4.3.2.1. offered to teachers

4.4. Analysis of collaborative practices

4.5. scoring/assessment

4.5.1. semi automated

4.5.2. automated

4.6. reorientation

4.6.1. explicability ??

4.6.2. see Paces Grenoble

4.7. examples

4.7.1. Pix

4.7.2. PACES Grenoble

4.7.3. ...

5. voice interaction

5.1. transcription pronounced sentences

5.1.1. speech2text input

5.1.2. Voice

5.1.3. Automatic subtitling

5.1.3.1. Almost real time

5.2. oral restitutions

5.2.1. text2speech

5.3. handicap

5.3.1. application pictogram

5.3.2. translation in LSF

5.3.3. voice input of texts

5.4. hands on another task

5.4.1. Technical training

5.5. Simultaneous translation

5.5.1. Skype

5.6. examples

5.6.1. chatbot

6. interaction textual

6.1. chatbot

6.2. uses natural language processing

6.2.1. understanding

6.2.2. sematic analysis

7. various disorders diagnosis

7.1. disorders

7.1.1. Dys

7.1.2. ASD

7.1.3. ...

7.2. On weak signals

7.3. identification from traces of activities

7.3.1. including textual

7.3.2. productions including oral productions

7.4. ...

8. pairing

8.1. pairing content

8.1.1. recommendation

8.1.2. video

8.1.2.1. Youtube

8.1.2.1.1. content provided by peers

8.1.2.2. Netflix

8.1.2.2.1. contents provided by editors

8.2. pairing individuals

8.2.1. peers for teamwork

8.2.1.1. Between students

8.2.1.2. Between

8.2.2. mentor or coach

8.2.2.1. students

8.2.2.1.1. peer

8.2.2.1.2. teachers

8.2.2.1.3. mentor

8.2.2.2. For Teachers

8.2.2.3. For executives

9. coaching students

9.1. learning behaviors

9.1.1. organized tion

9.1.1.1. procrastination

9.1.1.1.1. nudging

9.1.1.2. task planning

9.1.1.3. mémorisaiton

9.1.1.3.1. curve of forgetting

9.1.2. engagement

9.1.2.1. objectives

9.1.2.2. nudging

9.1.3. vision of the future

9.1.3.1. direction

9.1.3.2. commitment

9.1.4. anticipation dropping

9.1.4.1. on weak signals

9.2. guidance

9.2.1. probable trajectories

9.2.1.1. choices

9.2.1.2. corrective measures

9.2.2. choices

9.2.2.1. Corrective measures

10. stall diagnosis

10.1. based on school life data

10.1.1. delays and absences

10.1.2. comments

10.1.2.1. teachers

10.1.2.2. school life

10.1.3. evolution of grades

10.1.4. implication in facts

10.1.4.1. as perpetrator

10.1.4.2. as victim

10.1.5. sanctions

10.1.6. passage infirmary

10.1.6.1. attention given potentially sensitive

10.2. suggestion of remediation

10.2.1. based on the previous trajectories of pupils

11. Increase of documents

11.1. Examples

11.1.1. Google Doc

11.1.1.1. Voice

11.1.1.2. input Grammatical correction

11.1.1.3. Addition of illustrations

11.1.1.4. Translation

11.1.2. Google Sheet

11.1.2.1. Proposes formulas

11.1.2.2. Proposes cross sorting

11.1.2.3. Offers dataviz

11.1.2.4. translation

11.1.3. Google Slide

11.1.3.1. Voice input of slides comments input

11.1.4. Excel

11.1.4.1. Automatic analysis of tables

11.1.4.2. from a photo

11.1.5. equation solving tool

12. Attitude / behaviors Detection

12.1. attention and commitment

12.1.1. position

12.1.2. activity

12.1.2.1. Reading

12.1.2.2. Production

12.1.3. of facial expression

12.1.3.1. Detection of emotion

12.2. means

12.2.1. video

12.2.2. oculometry

12.2.3. sensors on seats

12.2.4. monitoring of movements

12.2.4.1. as in rugby

12.2.5. Text analysis

12.2.5.1. Detection of emotion

12.3. for

12.3.1. student

12.3.2. teachers

12.3.2.1. supervision

12.3.2.2. see example of sowing videos of supervision in Great Britain (seen at BETT 2019)

13. Text correction

13.1. Spelling

13.2. Grammar

13.3. translation of bad French into good French ...

14. system management

14.1. results prediction

14.1.1. by cohort

14.1.2. by establishment

14.1.3. by sector

14.1.4. Examples

14.1.4.1. Great Britain (seen at Bett)

14.2. HR prediction

14.2.1. absences

14.2.1.1. Anticipation

14.2.2. replacement

14.2.2.1. Anticipation

14.2.2.2. optimization

14.2.3. Training

14.2.4. Career trajectory

14.2.4.1. See student orientation!

14.3. Optimization of EdT

14.4. Optimization of school card measures

14.4.1. Opening

14.4.2. Closing

14.4.3. Transformation

14.5. identification of pbs various

14.5.1. syllabuses

14.5.2. program

14.5.3. activities

14.5.4. evaluations

14.5.5. suitability skills

14.6. Optimization of resources

14.6.1. Exam management (oral)

14.6.1.1. See Nancy

15. security management

15.1. adaptive filtering

15.2. See Parents in the area, Witigo, Block.si

15.2.1. to the individual

15.2.2. by geolocation

15.2.3. by content analysis

15.3. cyber harassment prevention

15.4. see Blocksi (fr) SmoothWall or GoGardian

15.4.1. victim

15.4.2. identification author identification

15.5. prevention radicalization

15.6. see Witigo (fr)

15.7. prevention of negative behavior

15.8. see Block.si

15.9. facial recognition

15.9.1. access management in the establishment

15.9.2. see 2 lycées ac Nice and Marseille

15.9.3. management of the call at the start of the course

15.10. sound listening

15.11. takes up the idea of 舃Saint Etienne (and many American cities): the establishment is already sound. We add microphones on the speakers. Listening (without permanent recording) detects "abnormal" signals (noise at night, fights, insults, intrusions, ...) and locates by triangulation the fact in progress to allow faster human intervention. A recording of the previous 3 minutes can also be considered, which implies another architecture. See the ethical aspects and RGPD (recognition of people for example?)

15.11.1. See Ville de Saint Etienne (link)

15.11.2. produced by Serenicity (link)

16. GRH / GPEC

16.1. recruitment of

16.1.1. teachers

16.1.1.1. in emergency phase

16.1.1.1.1. contractual

16.1.2. executives

16.1.2.1. by profile monitoring

16.2. initial

16.3. training continuing training

16.3.1. offer of training tailored

16.3.2. monitoring skills

16.3.2.1. badges

16.4. supervision / mentoring

16.4.1. pairing

16.4.1.1. mentor / teacher

16.4.1.2. peer / peer

16.5. coaching professors

17. SI management

17.1. attack detection

17.2. machine loads Optimization

17.3. data center Optimization

17.3.1. air conditioning

17.3.2. electrical consumption

17.3.3. anticipation Maintenance

17.3.4. Anticipation hardware

17.4. detection flaws

17.4.1. mobile application

17.4.2. applications coded

17.5. optimizationIaaS PaaS and SaaS

18. semanticization of texts

19. See Tellia

19.1. necessary for chatbots

19.2. evaluation of semantic richness

19.3. Syntactic structure

19.3.1. Intent

19.4. classification of texts

19.4.1. adaptive reading

19.4.1.1. to advance the reader

19.4.2. see Lexile of (link)

20. automated summaries

20.1. examples

20.1.1. SummarizeBot (link)

21. automated translations

21.1. with subtitles audio and video

21.2. type Youtube eg

21.2.1. accessibility

21.3. examples

21.3.1. Google Translate (link)

21.3.2. Bing Translator (link)

21.3.3. DeepL (link)

22. image recognition

22.1. Recognition of fake diplomas

22.2. handwriting

22.3. drawing

22.4. OCR

22.5. examples

22.5.1. Jamboard Google

23. indexing content

23.1. automatic

23.1.1. image

23.1.2. texts

23.1.3. sounds

23.1.4. videos

23.2. semi automatic

23.2.1. using reinforcement

23.3. suggestion of supplements

23.4. enrichment syllabus

23.5. examples

23.5.1. PerfectMemory

24. search engines

24.1. Delve on 365

24.2. CloudSearch on GSuite