
    Design an adaptive learning algorithm that determines the total learning potential across multiple difficulty paths for a student starting at their maximum capability level. Given an initial difficulty level, calculate the sum of all possible learning paths where each subsequent path decreases by two difficulty points from the previous level, continuing until reaching or going below the baseline difficulty of zero. The algorithm should account for all viable learning paths between the student's starting level and their minimum achievable level, ensuring comprehensive coverage of difficulty transitions. This calculation helps determine the overall learning exposure value for personalized educational planning and helps identify the optimal learning progression for each student.
    

    
def sum_series(n):
    """
    Design an adaptive learning algorithm that determines the total learning potential across multiple difficulty paths for a student starting at their maximum capability level. Given an initial difficulty level, calculate the sum of all possible learning paths where each subsequent path decreases by two difficulty points from the previous level, continuing until reaching or going below the baseline difficulty of zero. The algorithm should account for all viable learning paths between the student's starting level and their minimum achievable level, ensuring comprehensive coverage of difficulty transitions. This calculation helps determine the overall learning exposure value for personalized educational planning and helps identify the optimal learning progression for each student.
    

    """
def sum_series(n):
    """
    Design an adaptive learning algorithm that determines the total learning potential across multiple difficulty paths for a student starting at their maximum capability level. Given an initial difficulty level, calculate the sum of all possible learning paths where each subsequent path decreases by two difficulty points from the previous level, continuing until reaching or going below the baseline difficulty of zero. The algorithm should account for all viable learning paths between the student's starting level and their minimum achievable level, ensuring comprehensive coverage of difficulty transitions. This calculation helps determine the overall learning exposure value for personalized educational planning and helps identify the optimal learning progression for each student.
    

    """
    if n < 1:
        return 0
    else:
        return n + sum_series(n - 2)