Dialogue systems for mental health care aim to provide appropriate support to individuals experiencing mental distress. While extensive research has been conducted to deliver adequate emotional support, existing studies cannot identify individuals who require professional medical intervention and cannot offer suitable guidance. We introduce the Diagnostic Emotional Support Conversation task for an advanced mental health management system. We develop the DESC dataset to assess depression symptoms while maintaining user experience by utilizing task-specific utterance generation prompts and a strict filtering algorithm. Evaluations by professional psychological counselors indicate that DESC has a superior ability to diagnose depression than existing data. Additionally, conversational quality evaluation reveals that DESC maintains fluent, consistent, and coherent dialogues.

Like many students around the world, Eithne, 14, in Chorley, United Kingdom, was struggling to keep up in math at school after more than a year of COVID-19 related disruptions. In June 2021, her parents signed her up for a summer program offered by Eedi, an online math tutoring service. "Just dealing with lockdown, she hadn't had enough of a really good background," said her mother, Arianna. "She missed most of the Year 7 Maths, then Year 8. So, we thought, 'Let's give it a go, let's see where she needs a bit of help.'" Newly enrolled students on Eedi are asked to take a dynamic quiz of 10 multiple choice diagnostic questions that the service uses to learn where students struggle most in math.

Causal machine learning is a field that focuses on using machine learning method to tackle causality problems. Despite the recent progress of this field, there are still many unresolved challenges including missing data, selection bias, unobserved confounders, etc., which are ubiquitous in the real world. Advances in any of the above areas can greatly reduce the gap between research and real world impact. In this competition, we focus on two fundamental challenges of causal machine learning in the context of education using time-series data. The first is to identify the causal relationships between different constructs, where a construct is defined as the smallest element of learning. The second challenge is to predict the impact of learning one construct on the ability to answer questions on other constructs. Addressing these challenges will not only impact the causal ML community but also enable optimisation of students' knowledge acquisition, which can be deployed in a real edtech solution impacting millions of students. Participants will run these tasks in an idealised environment with synthetic data and a real-world scenario with evaluation data collected from a series of A/B tests. We expect participants to develop novel machine learning methodologies for causal discover between different constructs and the impact estimation of learning one construct on other constructs, which should bring fundamental advances to causal ML.

Like many students around the world, Eithne, 14, in Chorley, United Kingdom, was struggling to keep up in math at school after more than a year of COVID-19 related disruptions. In June 2021, her parents signed her up for a summer program offered by Eedi, an online math tutoring service. "Just dealing with lockdown, she hadn't had enough of a really good background," said her mother, Arianna. "She missed most of the Year 7 Maths, then Year 8. So, we thought, 'Let's give it a go, let's see where she needs a bit of help.'" Newly enrolled students on Eedi are asked to take a dynamic quiz of 10 multiple choice diagnostic questions that the service uses to learn where students struggle most in math. This information allows the service to place students on a learning pathway to overcome those specific obstacles, or misconceptions.

Every passing year marks the introduction of a technological advancement which affords some new form of progressive automation. Members of the millennial generation, like myself, are no strangers to the integration of robotic technology in daily life. I remember delighting in the introduction of self-checkout machines at the grocery store as a young kid, begging my grandmother to use the machines. Unfortunately, my old-fashioned grandmother never let me use the self-checkout, as she did not trust the technology to get the job done. New robotic technology has always made older generations understandably uneasy, especially since pop culture tends not to portray robots in the best light (who remembers The Stepford Wives or I, Robot?) Often, robotic technology is suspected of being too generalized, and unable to tailor tasks to an individual's specific needs.