WBCHSE Applied AI case studies
WBCHSE Applied AI · Put your knowledge to work in context.
Applied Artificial Intelligence is a vocational subject at the Higher Secondary level under the West Bengal Council of Higher Secondary Education. It is written for Class XI and XII students who want a school-level credential that pairs AI literacy with practical computing skill, rather than a purely theoretical science-stream course. The WBCHSE Applied AI case studies are 5 cases and about 5,229 words.
1Milk collection forecast: a 0.96 R² that failed in the first live week
A WBCHSE vocational team built a model to forecast how many litres of milk a block-level dairy cooperative should collect each morning. Their table had 720 daily records from January 2022 to December 2023 with columns: date, litres collected, day of week, festival flag, rainfall and local temperature.
975 words, about 5 min2Face-recognition attendance gate in a school with no biometric policy
A government-aided higher secondary school with 96 staff (teachers and non-teaching) installs a vendor-supplied face-recognition gate to mark staff attendance. The headmaster signs a one-year contract after the vendor shows a benchmark sheet claiming 99.2% accuracy.
996 words, about 5 min3The immunisation chart where the overall figure rises while both subgroups fall
A block health office publishes a two-page 'Immunisation at a glance' note. Page one is a bar chart titled 'Coverage is rising': five bars for 2019-2023 with the vertical axis starting at 88% and ending at 96%, so the 2022 bar looks about six times taller than the 2019 bar.
951 words, about 5 min4Handwritten Bengali account-opening forms: a 76% average that hides the dangerous field
A credit co-operative society in a small town receives about 1,400 handwritten savings-account opening forms a month, mostly in Bengali. Four staff type the details into a core banking system, taking roughly 6 minutes per form.
1,166 words, about 6 min5FasalMitra: 88% at home, 52% one village away, six weeks to a pitch
Three Class XII students build 'FasalMitra', a crop-disease classifier for their school's vocational exhibition. They photograph leaves in their own village and collect 620 images across five classes: 340 of bacterial leaf blight, 41 of a nutrient-deficiency class, and 239 spread over three other classes.
1,141 words, about 6 min